diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml
new file mode 100644
index 0000000..f3a9a39
--- /dev/null
+++ b/.github/workflows/docs.yaml
@@ -0,0 +1,29 @@
+name: Publish on PyPI
+
+on:
+  push:
+    tags:
+      - '*'
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.9
+
+      - name: Install dependencies
+        run: pip install --upgrade './[dev]'
+
+      - name: Build documentation
+        run: mkdocs gh-deploy
+
+      - name: Publish distribution to PyPI
+        uses: pypa/gh-action-pypi-publish@master
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/.vscode/settings.json b/.vscode/settings.json
index 3e44f84..4ffd601 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -73,5 +73,5 @@
   "python.linting.pylintEnabled": false,
   "python.linting.mypyEnabled": true,
   "python.testing.unittestEnabled": false,
-  "python.testing.pytestEnabled": true
+  "python.testing.pytestEnabled": true,
 }
diff --git a/README.md b/README.md
index e02a05b..886bfca 100644
--- a/README.md
+++ b/README.md
@@ -40,7 +40,15 @@ Find the dashboard at [http://localhost:6060](http://localhost:6060/dashboard/).
 
 ### Contribute
 
+
+#### Install
+
 ```sh
 pip install 'great-ai[dev]'
-
 ```
+
+#### Documentation
+
+```sh
+mkdocs serve
+```
\ No newline at end of file
diff --git a/docs/explanation.md b/docs/explanation.md
new file mode 100644
index 0000000..e69de29
diff --git a/docs/great_ai_example-main/.dockerignore b/docs/great_ai_example-main/.dockerignore
new file mode 100644
index 0000000..ccbef6a
--- /dev/null
+++ b/docs/great_ai_example-main/.dockerignore
@@ -0,0 +1,6 @@
+.venv
+.env
+**/.cache
+.git
+**/__pycache__
+.dockerignore
diff --git a/docs/great_ai_example-main/.gitignore b/docs/great_ai_example-main/.gitignore
new file mode 100644
index 0000000..cdadf66
--- /dev/null
+++ b/docs/great_ai_example-main/.gitignore
@@ -0,0 +1,10 @@
+.env
+.venv
+.DS_Store
+__pycache__
+.cache
+.mypy_cache
+.pytest_cache
+**/.ipynb_checkpoints
+**/tracing_database.json
+*.egg-info
diff --git a/docs/great_ai_example-main/.vscode/settings.json b/docs/great_ai_example-main/.vscode/settings.json
new file mode 100644
index 0000000..9a47044
--- /dev/null
+++ b/docs/great_ai_example-main/.vscode/settings.json
@@ -0,0 +1,10 @@
+{
+  "files.exclude": {
+    "**/__pycache__": true,
+    "**/.ipynb_checkpoints": true,
+    "**/.mypy_cache": true,
+    "**/.pytest_cache": true
+  },
+  "notebook.output.textLineLimit": 400,
+  "python.defaultInterpreterPath": ".env/bin/python"
+}
diff --git a/docs/great_ai_example-main/.vscode/tasks.json b/docs/great_ai_example-main/.vscode/tasks.json
new file mode 100644
index 0000000..17c41ee
--- /dev/null
+++ b/docs/great_ai_example-main/.vscode/tasks.json
@@ -0,0 +1,21 @@
+{
+  "version": "2.0.0",
+  "tasks": [
+    {
+      "label": "Format and lint",
+      "type": "shell",
+      "command": "source .env/bin/activate; ./format.sh .",
+      "windows": {
+        "command": ".env\\bin\\activate.bat; .\\format.sh ."
+      },
+      "group": "test",
+      "presentation": {
+        "reveal": "always",
+        "panel": "new"
+      },
+      "options": {
+        "cwd": "${workspaceFolder}"
+      }
+    }
+  ]
+}
diff --git a/docs/great_ai_example-main/Dockerfile b/docs/great_ai_example-main/Dockerfile
new file mode 100644
index 0000000..bd5c6cc
--- /dev/null
+++ b/docs/great_ai_example-main/Dockerfile
@@ -0,0 +1,11 @@
+FROM test
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir ./great_ai
+
+COPY mongo.ini .
+COPY deploy.ipynb .
+
+# RUN python3 -m large_file --backend s3 -secrets ~/.aws/credentials --cache my_first_file.json:3 my_second_file my_folder:0
+
+CMD ["deploy.ipynb"]
diff --git a/docs/great_ai_example-main/README.md b/docs/great_ai_example-main/README.md
new file mode 100644
index 0000000..d52e649
--- /dev/null
+++ b/docs/great_ai_example-main/README.md
@@ -0,0 +1,23 @@
+# Train a domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus)
+
+![steps](diagrams/scope.svg)
+
+## Install
+
+### System dependencies
+
+Make sure you have `python3`, `pip`, and `venv` installed.
+> On Ubuntu, execute: `sudo apt install -y python3 python3-pip python3-venv`
+
+### Install dependencies
+```sh
+python3 -m venv --copies .env 
+source .env/bin/activate
+
+pip install -r requirements.txt
+```
+## Execute 
+
+- [Part 1](src/data.ipynb)
+- [Part 2](src/train.ipynb)
+- [Part 3](src/deploy.ipynb)
diff --git a/docs/great_ai_example-main/diagrams/scope-data.svg b/docs/great_ai_example-main/diagrams/scope-data.svg
new file mode 100644
index 0000000..8e232ec
--- /dev/null
+++ b/docs/great_ai_example-main/diagrams/scope-data.svg
@@ -0,0 +1 @@
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text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 212px; height: 1px; padding-top: 140px; margin-left: 967px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 14px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><b>Deploy with standard<br /> organisational methods</b><br /><font style="font-size: 12px"> (SysAdmin, Ops, DevOps)</font></div></div></div></foreignObject><text x="1073" y="144" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="14px" text-anchor="middle">Deploy with standard...</text></switch></g><path d="M 922.5 140 L 969.63 140" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" stroke-dasharray="3 3" pointer-events="stroke"/><path d="M 974.88 140 L 967.88 143.5 L 969.63 140 L 967.88 136.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 740 280 L 760 220 L 950 220 L 930 280 Z" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 212px; height: 1px; padding-top: 140px; margin-left: 967px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 14px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><b>Deploy with standard<br /> organisational methods</b><br /><font style="font-size: 12px"> (SysAdmin, Ops, DevOps)</font></div></div></div></foreignObject><text x="1073" y="144" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="14px" text-anchor="middle">Deploy with standard...</text></switch></g><path d="M 922.5 140 L 969.63 140" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" stroke-dasharray="3 3" pointer-events="stroke"/><path d="M 974.88 140 L 967.88 143.5 L 969.63 140 L 967.88 136.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 740 280 L 760 220 L 950 220 L 930 280 Z" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 113px; height: 1px; padding-top: 153px; margin-left: 217px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><font style="font-size: 14px">Preprocessed data</font></div></div></div></foreignObject><text x="274" y="156" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Preprocessed data</text></switch></g><path d="M 170 140 L 209.63 140" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 214.88 140 L 207.88 143.5 L 209.63 140 L 207.88 136.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 0 180 L 20 100 L 180 100 L 160 180 Z" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 178px; height: 1px; padding-top: 140px; margin-left: 1px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><b><font style="font-size: 14px">Data acquisition</font></b><br />(Data Engineering)</div></div></div></foreignObject><text x="90" y="144" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Data acquisition...</text></switch></g><path d="M 526 140 L 560.63 140" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 565.88 140 L 558.88 143.5 L 560.63 140 L 558.88 136.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 376 180 L 396 100 L 536 100 L 516 180 Z" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 158px; height: 1px; padding-top: 140px; margin-left: 377px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><b><font style="font-size: 14px">Exploration &amp;<br /> model building</font></b><br />(Data Science)</div></div></div></foreignObject><text x="456" y="144" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Exploration &amp;...</text></switch></g><path d="M 639.5 190 L 639.92 240 Q 640 250 650 250 L 743.63 250" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" stroke-dasharray="3 3" pointer-events="stroke"/><path d="M 748.88 250 L 741.88 253.5 L 743.63 250 L 741.88 246.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 567 105 C 567 96.72 599.46 90 639.5 90 C 658.73 90 677.17 91.58 690.77 94.39 C 704.36 97.21 712 101.02 712 105 L 712 175 C 712 183.28 679.54 190 639.5 190 C 599.46 190 567 183.28 567 175 Z" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 712 105 C 712 113.28 679.54 120 639.5 120 C 599.46 120 567 113.28 567 105" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 143px; height: 1px; padding-top: 153px; margin-left: 568px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><font style="font-size: 14px">Trained models  &amp; prototype inference code</font></div></div></div></foreignObject><text x="640" y="156" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Trained models  &amp; protot...</text></switch></g><path d="M 639.5 90 L 639.92 40 Q 640 30 650 30 L 743.63 30" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" stroke-dasharray="3 3" pointer-events="stroke"/><path d="M 748.88 30 L 741.88 33.5 L 743.63 30 L 741.88 26.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 966 180 L 986 100 L 1180 100 L 1160 180 Z" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 212px; height: 1px; padding-top: 140px; margin-left: 967px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 14px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><b>Deploy with standard<br /> organisational methods</b><br /><font style="font-size: 12px"> (SysAdmin, Ops, DevOps)</font></div></div></div></foreignObject><text x="1073" y="144" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="14px" text-anchor="middle">Deploy with standard...</text></switch></g><path d="M 922.5 140 L 969.63 140" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" stroke-dasharray="3 3" pointer-events="stroke"/><path d="M 974.88 140 L 967.88 143.5 L 969.63 140 L 967.88 136.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 740 280 L 760 220 L 950 220 L 930 280 Z" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 208px; height: 1px; padding-top: 250px; margin-left: 741px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 14px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b>Use an AI deployment <br />SaaS/PaaS </b><br /><font style="font-size: 12px">(MLEM, Akkio, etc.) </font></div></div></div></foreignObject><text x="845" y="254" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="14px" text-anchor="middle">Use an AI deployment...</text></switch></g><path d="M 740 60 L 760 0 L 950 0 L 930 60 Z" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 208px; height: 1px; padding-top: 30px; margin-left: 741px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><b style="font-size: 14px">Transforming into<br /> production-ready service</b><br />(MLOps)</div></div></div></foreignObject><text x="845" y="34" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Transforming into...</text></switch></g><path d="M 845 190 L 845 213.63" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" stroke-dasharray="3 3" pointer-events="none"/><path d="M 845 218.88 L 841.5 211.88 L 845 213.63 L 848.5 211.88 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 845 190 L 845 213.63" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 845 218.88 L 841.5 211.88 L 845 213.63 L 848.5 211.88 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 767.5 105 C 767.5 96.72 802.2 90 845 90 C 865.55 90 885.27 91.58 899.8 94.39 C 914.33 97.21 922.5 101.02 922.5 105 L 922.5 175 C 922.5 183.28 887.8 190 845 190 C 802.2 190 767.5 183.28 767.5 175 Z" fill="rgb(255, 255, 255)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 922.5 105 C 922.5 113.28 887.8 120 845 120 C 802.2 120 767.5 113.28 767.5 105" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 153px; height: 1px; padding-top: 153px; margin-left: 769px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: none; white-space: normal; overflow-wrap: normal;"><font style="font-size: 14px">Artifact combining the model with deplyoment best-practices</font></div></div></div></foreignObject><text x="845" y="156" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="12px" text-anchor="middle">Artifact combining the mod...</text></switch></g><path d="M 845 60 L 845 83.63" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/><path d="M 845 88.88 L 841.5 81.88 L 845 83.63 L 848.5 81.88 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="none"/></g><switch><g requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"/><a transform="translate(0,-5)" xlink:href="https://www.diagrams.net/doc/faq/svg-export-text-problems" target="_blank"><text text-anchor="middle" font-size="10px" x="50%" y="100%">Viewer does not support full SVG 1.1</text></a></switch></svg>
\ No newline at end of file
diff --git a/docs/great_ai_example-main/format.sh b/docs/great_ai_example-main/format.sh
new file mode 100755
index 0000000..737a2b9
--- /dev/null
+++ b/docs/great_ai_example-main/format.sh
@@ -0,0 +1,31 @@
+#!/bin/sh
+
+set -e
+
+echo "Installing dependencies if necessary"
+python3 -m pip install --upgrade autoflake isort black[jupyter] mypy flake8
+
+echo "Formatting and checking $1"
+
+cd $1
+
+echo Running autoflake
+python3 -m autoflake --expand-star-imports --remove-all-unused-imports --ignore-init-module-imports --remove-unused-variables --in-place -r .
+
+echo Running isort
+python3 -m isort --profile black --skip .env .
+
+echo Running black
+python3 -m black . --exclude .env
+
+if ls *.py 1> /dev/null 2>&1; then
+    echo Running mypy
+    python3 -m mypy --namespace-packages --ignore-missing-imports --install-types --non-interactive --disallow-untyped-defs --disallow-incomplete-defs --pretty --follow-imports=silent --exclude=external/ --exclude=/build/ .
+fi
+
+echo Running Flake8
+python3 -m flake8 . --count --show-source --statistics --exclude=__init__.py,.env,external --ignore=E501,E722,E402,W503,E203
+
+cd -
+
+echo "Finished formatting"
diff --git a/docs/great_ai_example-main/requirements.txt b/docs/great_ai_example-main/requirements.txt
new file mode 100644
index 0000000..dfcc2e6
--- /dev/null
+++ b/docs/great_ai_example-main/requirements.txt
@@ -0,0 +1,4 @@
+great_ai
+notebook
+nbformat
+nbconvert
\ No newline at end of file
diff --git a/docs/great_ai_example-main/src/data.ipynb b/docs/great_ai_example-main/src/data.ipynb
new file mode 100644
index 0000000..1443c7c
--- /dev/null
+++ b/docs/great_ai_example-main/src/data.ipynb
@@ -0,0 +1,265 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train a domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus)\n",
+    "> Part 1: obtain and clean data\n",
+    "\n",
+    "![position of this step in the lifecycle](../diagrams/scope-data.svg)\n",
+    "> The blue boxes show the steps implemented in this notebook.\n",
+    "\n",
+    "### Extract\n",
+    "\n",
+    "This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data.\n",
+    "\n",
+    "In this case, we download the semantic scholar dataset from a public S3 bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MAX_CHUNK_COUNT = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Processing 1 out of the 6002 available chunks'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import urllib.request\n",
+    "from random import shuffle\n",
+    "\n",
+    "manifest = (\n",
+    "    urllib.request.urlopen(\n",
+    "        \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\"\n",
+    "    )\n",
+    "    .read()\n",
+    "    .decode()\n",
+    ")  # a list of available chunks separated by '\\n' characters\n",
+    "\n",
+    "lines = manifest.split()\n",
+    "shuffle(lines)\n",
+    "chunks = lines[:MAX_CHUNK_COUNT]\n",
+    "\n",
+    "f\"Processing {len(chunks)} out of the {len(manifest.split())} available chunks\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transform\n",
+    "\n",
+    "- Filter out non-English abstracts using `great_ai.utilities.predict_language`\n",
+    "- Project it to only keep the necessary components (text and labels), clean the textual content using `great_ai.utilities.clean`\n",
+    "- We will speed up processing using `great_ai.utilities.parallel_map`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Spacy model en_core_web_sm not found locally, downloading...\n",
+      "Collecting en-core-web-sm==3.3.0\n",
+      "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.3.0/en_core_web_sm-3.3.0-py3-none-any.whl (12.8 MB)\n",
+      "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.8/12.8 MB 3.6 MB/s eta 0:00:00\n",
+      "Requirement already satisfied: spacy<3.4.0,>=3.3.0.dev0 in ./.env/lib/python3.10/site-packages (from en-core-web-sm==3.3.0) (3.3.1)\n",
+      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (4.64.0)\n",
+      "Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (0.9.1)\n",
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+      "Requirement already satisfied: blis<0.8.0,>=0.4.0 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (0.7.8)\n",
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+      "Requirement already satisfied: numpy>=1.15.0 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (1.23.0)\n",
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+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in ./.env/lib/python3.10/site-packages (from pydantic!=1.8,!=1.8.1,<1.9.0,>=1.7.4->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (4.2.0)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in ./.env/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (2.0.12)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in ./.env/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (2022.6.15)\n",
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+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./.env/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (1.26.9)\n",
+      "Requirement already satisfied: click<9.0.0,>=7.1.1 in ./.env/lib/python3.10/site-packages (from typer<0.5.0,>=0.3.0->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (8.1.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in ./.env/lib/python3.10/site-packages (from jinja2->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (2.1.1)\n",
+      "Installing collected packages: en-core-web-sm\n",
+      "Successfully installed en-core-web-sm-3.3.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;5;226m2022-06-25 14:21:57,983 |  WARNING | Limiting concurrency to 1 because there are only 1 chunks\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:21:57,984 |     INFO | Starting parallel map (concurrency: 1, chunk size: 1)\u001b[0m\n",
+      "\u001b[38;5;226m2022-06-25 14:21:57,984 |  WARNING | Running in series, there is no reason for parallelism\u001b[0m\n",
+      "100%|██████████| 1/1 [03:26<00:00, 206.86s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import List, Tuple\n",
+    "import json\n",
+    "import gzip\n",
+    "from great_ai import parallel_map, clean, is_english, predict_language\n",
+    "\n",
+    "\n",
+    "def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]:\n",
+    "    # Extract\n",
+    "    response = urllib.request.urlopen(\n",
+    "        f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/{chunk_key}\"\n",
+    "    )  # a gzipped JSON Lines file\n",
+    "\n",
+    "    decompressed = gzip.decompress(response.read())\n",
+    "    decoded = decompressed.decode()\n",
+    "    chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line]\n",
+    "\n",
+    "    # Transform\n",
+    "    return [\n",
+    "        (\n",
+    "            clean(\n",
+    "                f'{c[\"title\"]} {c[\"paperAbstract\"]} {c[\"journalName\"]} {c[\"venue\"]}',\n",
+    "                convert_to_ascii=True,\n",
+    "            ),  # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts\n",
+    "            c[\"fieldsOfStudy\"],\n",
+    "        )  # Create pairs of `(text, [...domains])`\n",
+    "        for c in chunk\n",
+    "        if c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))\n",
+    "    ]\n",
+    "\n",
+    "\n",
+    "preprocessed_chunks = parallel_map(preprocess_chunk, chunks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import chain\n",
+    "\n",
+    "preprocessed_data = list(chain(*preprocessed_chunks))\n",
+    "X, y = zip(\n",
+    "    *preprocessed_data\n",
+    ")  # X is the input, y is the expected (ground truth) output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load\n",
+    "\n",
+    "Upload the dataset (or a part of it) to a central repository using `great_ai.add_ground_truth`. This step automatically tags each datapoint with a split label according to the ratios we set. Additional tags can be also given.\n",
+    "\n",
+    "#### Production-ready backend\n",
+    "\n",
+    "The MongoDB driver is automatically configured if  `mongo.ini` exists with the following scheme:\n",
+    "\n",
+    "```ini\n",
+    "mongo_connection_string=mongodb://localhost:27017/\n",
+    "mongo_database=my_great_ai_db\n",
+    "```\n",
+    "> You can install MongoDB from [here](https://www.mongodb.com/docs/manual/installation) or [use it as a service](https://www.mongodb.com/cloud/atlas/register)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;5;226m2022-06-25 14:25:24,989 |  WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:25:24,990 |     INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:25:24,991 |     INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:25:24,992 |     INFO | Settings: configured ✅\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:25:24,993 |     INFO | 🔩 tracing_database: MongodbDriver\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:25:24,994 |     INFO | 🔩 large_file_implementation: LargeFileMongo\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:25:24,994 |     INFO | 🔩 is_production: False\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:25:24,995 |     INFO | 🔩 should_log_exception_stack: True\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:25:24,996 |     INFO | 🔩 prediction_cache_size: 512\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:25:24,997 |     INFO | 🔩 dashboard_table_size: 50\u001b[0m\n",
+      "\u001b[38;5;226m2022-06-25 14:25:24,998 |  WARNING | You still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
+      "\u001b[38;5;226m2022-06-25 14:25:24,998 |  WARNING | > Find out more at https://se-ml.github.io/practices/\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "from great_ai import add_ground_truth\n",
+    "\n",
+    "add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Next: [Part 2](train.ipynb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 ('.env': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "c1f394f9662881005685eeb18d8f9f77079b1b8b9a5ece1f825bfa01fcb7f52f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/great_ai_example-main/src/deploy.ipynb b/docs/great_ai_example-main/src/deploy.ipynb
new file mode 100644
index 0000000..a8acbfe
--- /dev/null
+++ b/docs/great_ai_example-main/src/deploy.ipynb
@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train a domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus)\n",
+    "\n",
+    "> Part 3: Create production inference function\n",
+    "\n",
+    "![position of this step in the lifecycle](../diagrams/scope-deploy.svg)\n",
+    "> The blue boxes show the steps implemented in this notebook.\n",
+    "\n",
+    "In [Part 2](train.ipynb), we trained our AI model. Now, it's time to create **G**eneral **R**obust **E**nd-to-end **A**utomated **T**rustworthy deployment from it using the `GreatAI` Python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;5;226m2022-06-25 14:57:20,629 |  WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,629 |     INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,630 |     INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,631 |     INFO | Settings: configured ✅\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,631 |     INFO | 🔩 tracing_database: MongodbDriver\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,631 |     INFO | 🔩 large_file_implementation: LargeFileMongo\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,632 |     INFO | 🔩 is_production: False\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,633 |     INFO | 🔩 should_log_exception_stack: True\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,633 |     INFO | 🔩 prediction_cache_size: 512\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,634 |     INFO | 🔩 dashboard_table_size: 50\u001b[0m\n",
+      "\u001b[38;5;226m2022-06-25 14:57:20,634 |  WARNING | You still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
+      "\u001b[38;5;226m2022-06-25 14:57:20,635 |  WARNING | > Find out more at https://se-ml.github.io/practices/\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,642 |     INFO | Latest version of small-domain-prediction is 0 (from versions: 0)\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:57:20,643 |     INFO | File small-domain-prediction-0 found in cache\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import numpy as np\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from great_ai.utilities import clean\n",
+    "from great_ai import (\n",
+    "    MultiLabelClassificationOutput,\n",
+    "    ClassificationOutput,\n",
+    "    GreatAI,\n",
+    "    use_model,\n",
+    "    parameter,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "@GreatAI.create\n",
+    "@use_model(\"small-domain-prediction\", version=\"latest\")\n",
+    "@parameter(\"target_confidence\", validator=lambda c: 0 <= c <= 100)\n",
+    "def predict_domain(\n",
+    "    text: str, model: Pipeline, target_confidence: int = 50\n",
+    ") -> MultiLabelClassificationOutput:\n",
+    "    \"\"\"\n",
+    "    Predict the scientific domain of the input text.\n",
+    "    Return labels until their sum likelihood is larger than `target_confidence`.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True))\n",
+    "    features = model.named_steps[\"vectorizer\"].transform([preprocessed])\n",
+    "    prediction = model.named_steps[\"classifier\"].predict_proba(features)[0]\n",
+    "\n",
+    "    best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True)\n",
+    "\n",
+    "    results = MultiLabelClassificationOutput()\n",
+    "    for class_index, probability in best_classes:\n",
+    "        results.labels.append(\n",
+    "            get_label(\n",
+    "                model=model,\n",
+    "                features=features,\n",
+    "                class_index=class_index,\n",
+    "                probability=probability,\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "        if sum(r.confidence for r in results.labels) >= target_confidence:\n",
+    "            break\n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "def get_label(\n",
+    "    model: Pipeline, features: np.ndarray, class_index: int, probability: float\n",
+    ") -> ClassificationOutput:\n",
+    "    return ClassificationOutput(\n",
+    "        label=model.named_steps[\"classifier\"].classes_[class_index],\n",
+    "        confidence=round(probability * 100),\n",
+    "        explanation=[\n",
+    "            word\n",
+    "            for _, word in sorted(\n",
+    "                (\n",
+    "                    (weight, word)\n",
+    "                    for weight, word, count in zip(\n",
+    "                        model.named_steps[\"classifier\"].feature_log_prob_[class_index],\n",
+    "                        model.named_steps[\"vectorizer\"].get_feature_names_out(),\n",
+    "                        features.A[0],\n",
+    "                    )\n",
+    "                    if count > 0\n",
+    "                ),\n",
+    "                reverse=True,\n",
+    "            )\n",
+    "        ][:5],\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check accuracy on the test split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;5;39m2022-06-25 14:57:21,004 |     INFO | Starting parallel map (concurrency: 12, chunk size: 26)\u001b[0m\n",
+      "100%|██████████| 3054/3054 [00:31<00:00, 96.38it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                       precision    recall  f1-score   support\n",
+      "\n",
+      "                  Art       0.35      0.27      0.31        33\n",
+      "              Biology       0.77      0.84      0.80       299\n",
+      "             Business       0.44      0.61      0.51        82\n",
+      "            Chemistry       0.79      0.67      0.72       289\n",
+      "     Computer Science       0.74      0.79      0.77       341\n",
+      "            Economics       0.65      0.56      0.60        61\n",
+      "          Engineering       0.53      0.52      0.53       202\n",
+      "Environmental Science       0.51      0.49      0.50        61\n",
+      "            Geography       0.54      0.26      0.35        58\n",
+      "              Geology       0.69      0.56      0.62        48\n",
+      "              History       0.36      0.17      0.23        29\n",
+      "    Materials Science       0.68      0.83      0.74       248\n",
+      "          Mathematics       0.77      0.57      0.66       122\n",
+      "             Medicine       0.97      0.79      0.87       715\n",
+      "           Philosophy       0.50      0.05      0.10        19\n",
+      "              Physics       0.72      0.75      0.74       162\n",
+      "    Political Science       0.39      0.49      0.43        57\n",
+      "           Psychology       0.49      0.81      0.61       141\n",
+      "            Sociology       0.32      0.53      0.40        87\n",
+      "\n",
+      "             accuracy                           0.70      3054\n",
+      "            macro avg       0.59      0.56      0.55      3054\n",
+      "         weighted avg       0.72      0.70      0.70      3054\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2160x1080 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if __name__ == \"__main__\":\n",
+    "    from great_ai import query_ground_truth\n",
+    "    from sklearn import metrics\n",
+    "\n",
+    "    data = query_ground_truth(\"test\")\n",
+    "\n",
+    "    X = [d.input for d in data]\n",
+    "    y_actual = [d.feedback for d in data]\n",
+    "\n",
+    "    y_predicted = [d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True)]\n",
+    "    y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)]\n",
+    "\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    # Configure matplotlib to have nice, high-resolution charts\n",
+    "    %matplotlib inline\n",
+    "\n",
+    "    plt.rcParams[\"figure.figsize\"] = (30, 15)\n",
+    "    plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
+    "    plt.rcParams[\"font.size\"] = 12\n",
+    "    plt.rcParams[\"axes.xmargin\"] = 0\n",
+    "\n",
+    "    print(metrics.classification_report(y_actual_aligned, y_predicted))\n",
+    "    metrics.ConfusionMatrixDisplay.from_predictions(\n",
+    "        y_true=y_actual_aligned,\n",
+    "        y_pred=y_predicted,\n",
+    "        xticks_rotation=\"vertical\",\n",
+    "        normalize=\"pred\",\n",
+    "        values_format=\".2f\",\n",
+    "    )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 ('.env': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "c1f394f9662881005685eeb18d8f9f77079b1b8b9a5ece1f825bfa01fcb7f52f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/great_ai_example-main/src/mongo.ini b/docs/great_ai_example-main/src/mongo.ini
new file mode 100644
index 0000000..8d22e1c
--- /dev/null
+++ b/docs/great_ai_example-main/src/mongo.ini
@@ -0,0 +1,2 @@
+mongo_connection_string=mongodb://localhost:27017/  # change this
+mongo_database=great_ai_example  # this will be automatically created
diff --git a/docs/great_ai_example-main/src/train.ipynb b/docs/great_ai_example-main/src/train.ipynb
new file mode 100644
index 0000000..fb68cda
--- /dev/null
+++ b/docs/great_ai_example-main/src/train.ipynb
@@ -0,0 +1,1811 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train a domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus)\n",
+    "\n",
+    "> Part 2: train a model\n",
+    "\n",
+    "![position of this step in the lifecycle](../diagrams/scope-train.svg)\n",
+    "> The blue boxes show the steps implemented in this notebook.\n",
+    "\n",
+    "In [Part 1](data.ipynb), we have cleaned and transformed our training data. We can now access this data using `great_ai.LargeFile`. Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3. \n",
+    "\n",
+    "In this part, we hyperparameter-optimise and train a simple, Naive Bayes classifier which we then export for deployment using `great_ai.save_model`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load data that has been extracted in [part 1](data.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;5;226m2022-06-25 14:50:29,879 |  WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:29,880 |     INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:29,881 |     INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:29,881 |     INFO | Settings: configured ✅\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:29,882 |     INFO | 🔩 tracing_database: MongodbDriver\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:29,883 |     INFO | 🔩 large_file_implementation: LargeFileMongo\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:29,883 |     INFO | 🔩 is_production: False\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:29,884 |     INFO | 🔩 should_log_exception_stack: True\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:29,884 |     INFO | 🔩 prediction_cache_size: 512\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:29,885 |     INFO | 🔩 dashboard_table_size: 50\u001b[0m\n",
+      "\u001b[38;5;226m2022-06-25 14:50:29,885 |  WARNING | You still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
+      "\u001b[38;5;226m2022-06-25 14:50:29,885 |  WARNING | > Find out more at https://se-ml.github.io/practices/\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "from great_ai import query_ground_truth\n",
+    "\n",
+    "data = query_ground_truth(\"train\")\n",
+    "X = [d.input for d in data for domain in d.feedback]\n",
+    "y = [domain for d in data for domain in d.feedback]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "domain=%{x}<br>count=%{y}<extra></extra>",
+         "legendgroup": "",
+         "marker": {
+          "color": "#636efa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "",
+         "offsetgroup": "",
+         "orientation": "v",
+         "showlegend": false,
+         "textposition": "auto",
+         "type": "bar",
+         "x": [
+          "Medicine",
+          "Computer Science",
+          "Chemistry",
+          "Biology",
+          "Materials Science",
+          "Engineering",
+          "Physics",
+          "Psychology",
+          "Mathematics",
+          "Sociology",
+          "Business",
+          "Political Science",
+          "Economics",
+          "Geology",
+          "Geography",
+          "Environmental Science",
+          "History",
+          "Art",
+          "Philosophy"
+         ],
+         "xaxis": "x",
+         "y": [
+          4200,
+          1371,
+          1307,
+          1300,
+          1111,
+          876,
+          639,
+          617,
+          569,
+          334,
+          323,
+          311,
+          269,
+          256,
+          245,
+          244,
+          143,
+          137,
+          62
+         ],
+         "yaxis": "y"
+        }
+       ],
+       "layout": {
+        "barmode": "relative",
+        "height": 400,
+        "legend": {
+         "tracegroupgap": 0
+        },
+        "margin": {
+         "t": 60
+        },
+        "template": {
+         "data": {
+          "bar": [
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+           "title": {
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+           "zerolinewidth": 2
+          }
+         }
+        },
+        "width": 1200,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
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+         "title": {
+          "text": "domain"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
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+         "title": {
+          "text": "count"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from collections import Counter\n",
+    "import plotly.express as px\n",
+    "\n",
+    "df = pd.DataFrame(Counter(y).most_common(), columns=[\"domain\", \"count\"])\n",
+    "px.bar(df, \"domain\", \"count\", width=1200, height=400).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optimise and train Multinomial Naive Bayes classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "\n",
+    "\n",
+    "def create_pipeline() -> Pipeline:\n",
+    "    return Pipeline(\n",
+    "        steps=[\n",
+    "            (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)),\n",
+    "            (\"classifier\", MultinomialNB()),\n",
+    "        ]\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 3 folds for each of 24 candidates, totalling 72 fits\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_classifier__alpha</th>\n",
+       "      <th>param_classifier__fit_prior</th>\n",
+       "      <th>param_vectorizer__max_df</th>\n",
+       "      <th>param_vectorizer__min_df</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.962260</td>\n",
+       "      <td>0.147449</td>\n",
+       "      <td>0.935357</td>\n",
+       "      <td>0.063659</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.485030</td>\n",
+       "      <td>0.463849</td>\n",
+       "      <td>0.481840</td>\n",
+       "      <td>0.476906</td>\n",
+       "      <td>0.009324</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.942605</td>\n",
+       "      <td>0.111027</td>\n",
+       "      <td>0.952361</td>\n",
+       "      <td>0.066812</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.482890</td>\n",
+       "      <td>0.459556</td>\n",
+       "      <td>0.479362</td>\n",
+       "      <td>0.473936</td>\n",
+       "      <td>0.010270</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>2.145152</td>\n",
+       "      <td>0.068978</td>\n",
+       "      <td>1.002291</td>\n",
+       "      <td>0.047358</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.467330</td>\n",
+       "      <td>0.442994</td>\n",
+       "      <td>0.464302</td>\n",
+       "      <td>0.458208</td>\n",
+       "      <td>0.010829</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1.971888</td>\n",
+       "      <td>0.126950</td>\n",
+       "      <td>0.739795</td>\n",
+       "      <td>0.071551</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.454830</td>\n",
+       "      <td>0.422902</td>\n",
+       "      <td>0.450677</td>\n",
+       "      <td>0.442803</td>\n",
+       "      <td>0.014174</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.861275</td>\n",
+       "      <td>0.013389</td>\n",
+       "      <td>1.058907</td>\n",
+       "      <td>0.111122</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.456127</td>\n",
+       "      <td>0.422456</td>\n",
+       "      <td>0.443827</td>\n",
+       "      <td>0.440803</td>\n",
+       "      <td>0.013912</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.825397</td>\n",
+       "      <td>0.105754</td>\n",
+       "      <td>0.892227</td>\n",
+       "      <td>0.057003</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.438232</td>\n",
+       "      <td>0.440464</td>\n",
+       "      <td>0.422667</td>\n",
+       "      <td>0.433788</td>\n",
+       "      <td>0.007916</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1.693333</td>\n",
+       "      <td>0.009667</td>\n",
+       "      <td>0.501491</td>\n",
+       "      <td>0.006545</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.433915</td>\n",
+       "      <td>0.439470</td>\n",
+       "      <td>0.416031</td>\n",
+       "      <td>0.429805</td>\n",
+       "      <td>0.010001</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.008045</td>\n",
+       "      <td>0.145330</td>\n",
+       "      <td>0.944559</td>\n",
+       "      <td>0.155925</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.436178</td>\n",
+       "      <td>0.425724</td>\n",
+       "      <td>0.418396</td>\n",
+       "      <td>0.426766</td>\n",
+       "      <td>0.007297</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.749200</td>\n",
+       "      <td>0.022959</td>\n",
+       "      <td>0.889532</td>\n",
+       "      <td>0.047517</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.428215</td>\n",
+       "      <td>0.425398</td>\n",
+       "      <td>0.411051</td>\n",
+       "      <td>0.421555</td>\n",
+       "      <td>0.007516</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.960889</td>\n",
+       "      <td>0.098004</td>\n",
+       "      <td>0.985957</td>\n",
+       "      <td>0.080925</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.430638</td>\n",
+       "      <td>0.406619</td>\n",
+       "      <td>0.420213</td>\n",
+       "      <td>0.419157</td>\n",
+       "      <td>0.009834</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1.807799</td>\n",
+       "      <td>0.064891</td>\n",
+       "      <td>0.881872</td>\n",
+       "      <td>0.030810</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.402402</td>\n",
+       "      <td>0.372353</td>\n",
+       "      <td>0.386189</td>\n",
+       "      <td>0.386981</td>\n",
+       "      <td>0.012280</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.009232</td>\n",
+       "      <td>0.043125</td>\n",
+       "      <td>0.899676</td>\n",
+       "      <td>0.036977</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.389797</td>\n",
+       "      <td>0.372619</td>\n",
+       "      <td>0.388358</td>\n",
+       "      <td>0.383591</td>\n",
+       "      <td>0.007781</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.868087</td>\n",
+       "      <td>0.094739</td>\n",
+       "      <td>1.005353</td>\n",
+       "      <td>0.101466</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.379323</td>\n",
+       "      <td>0.364667</td>\n",
+       "      <td>0.379060</td>\n",
+       "      <td>0.374350</td>\n",
+       "      <td>0.006848</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1.958430</td>\n",
+       "      <td>0.039639</td>\n",
+       "      <td>0.890963</td>\n",
+       "      <td>0.012546</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.366936</td>\n",
+       "      <td>0.343361</td>\n",
+       "      <td>0.363883</td>\n",
+       "      <td>0.358060</td>\n",
+       "      <td>0.010468</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.940692</td>\n",
+       "      <td>0.018320</td>\n",
+       "      <td>0.898865</td>\n",
+       "      <td>0.030651</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.354850</td>\n",
+       "      <td>0.349237</td>\n",
+       "      <td>0.342737</td>\n",
+       "      <td>0.348941</td>\n",
+       "      <td>0.004950</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.855691</td>\n",
+       "      <td>0.029506</td>\n",
+       "      <td>0.866492</td>\n",
+       "      <td>0.038048</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.349005</td>\n",
+       "      <td>0.341832</td>\n",
+       "      <td>0.328617</td>\n",
+       "      <td>0.339818</td>\n",
+       "      <td>0.008444</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.798559</td>\n",
+       "      <td>0.103497</td>\n",
+       "      <td>0.888273</td>\n",
+       "      <td>0.069050</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.312332</td>\n",
+       "      <td>0.297655</td>\n",
+       "      <td>0.307471</td>\n",
+       "      <td>0.305819</td>\n",
+       "      <td>0.006104</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2.016041</td>\n",
+       "      <td>0.232138</td>\n",
+       "      <td>0.967630</td>\n",
+       "      <td>0.146144</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.304942</td>\n",
+       "      <td>0.296921</td>\n",
+       "      <td>0.297121</td>\n",
+       "      <td>0.299661</td>\n",
+       "      <td>0.003735</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1.829513</td>\n",
+       "      <td>0.112645</td>\n",
+       "      <td>0.885848</td>\n",
+       "      <td>0.027726</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.301539</td>\n",
+       "      <td>0.285396</td>\n",
+       "      <td>0.297272</td>\n",
+       "      <td>0.294736</td>\n",
+       "      <td>0.006830</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.905362</td>\n",
+       "      <td>0.018052</td>\n",
+       "      <td>0.885552</td>\n",
+       "      <td>0.023985</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.295635</td>\n",
+       "      <td>0.276759</td>\n",
+       "      <td>0.296270</td>\n",
+       "      <td>0.289555</td>\n",
+       "      <td>0.009052</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.793688</td>\n",
+       "      <td>0.049995</td>\n",
+       "      <td>0.921301</td>\n",
+       "      <td>0.060980</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.286746</td>\n",
+       "      <td>0.272260</td>\n",
+       "      <td>0.277084</td>\n",
+       "      <td>0.278696</td>\n",
+       "      <td>0.006023</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.078568</td>\n",
+       "      <td>0.045549</td>\n",
+       "      <td>0.963691</td>\n",
+       "      <td>0.048281</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.276452</td>\n",
+       "      <td>0.265509</td>\n",
+       "      <td>0.268949</td>\n",
+       "      <td>0.270303</td>\n",
+       "      <td>0.004569</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.839506</td>\n",
+       "      <td>0.048910</td>\n",
+       "      <td>0.921812</td>\n",
+       "      <td>0.010374</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.183196</td>\n",
+       "      <td>0.186144</td>\n",
+       "      <td>0.180323</td>\n",
+       "      <td>0.183221</td>\n",
+       "      <td>0.002376</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1.909279</td>\n",
+       "      <td>0.110639</td>\n",
+       "      <td>1.056087</td>\n",
+       "      <td>0.129738</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.165277</td>\n",
+       "      <td>0.165840</td>\n",
+       "      <td>0.167088</td>\n",
+       "      <td>0.166068</td>\n",
+       "      <td>0.000757</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "7        1.962260      0.147449         0.935357        0.063659   \n",
+       "10       1.942605      0.111027         0.952361        0.066812   \n",
+       "19       2.145152      0.068978         1.002291        0.047358   \n",
+       "22       1.971888      0.126950         0.739795        0.071551   \n",
+       "6        1.861275      0.013389         1.058907        0.111122   \n",
+       "11       1.825397      0.105754         0.892227        0.057003   \n",
+       "23       1.693333      0.009667         0.501491        0.006545   \n",
+       "8        2.008045      0.145330         0.944559        0.155925   \n",
+       "20       1.749200      0.022959         0.889532        0.047517   \n",
+       "9        1.960889      0.098004         0.985957        0.080925   \n",
+       "18       1.807799      0.064891         0.881872        0.030810   \n",
+       "1        2.009232      0.043125         0.899676        0.036977   \n",
+       "4        1.868087      0.094739         1.005353        0.101466   \n",
+       "21       1.958430      0.039639         0.890963        0.012546   \n",
+       "5        1.940692      0.018320         0.898865        0.030651   \n",
+       "2        1.855691      0.029506         0.866492        0.038048   \n",
+       "17       1.798559      0.103497         0.888273        0.069050   \n",
+       "14       2.016041      0.232138         0.967630        0.146144   \n",
+       "13       1.829513      0.112645         0.885848        0.027726   \n",
+       "0        1.905362      0.018052         0.885552        0.023985   \n",
+       "16       1.793688      0.049995         0.921301        0.060980   \n",
+       "3        2.078568      0.045549         0.963691        0.048281   \n",
+       "12       1.839506      0.048910         0.921812        0.010374   \n",
+       "15       1.909279      0.110639         1.056087        0.129738   \n",
+       "\n",
+       "   param_classifier__alpha param_classifier__fit_prior  \\\n",
+       "7                      0.5                       False   \n",
+       "10                     0.5                       False   \n",
+       "19                       1                       False   \n",
+       "22                       1                       False   \n",
+       "6                      0.5                       False   \n",
+       "11                     0.5                       False   \n",
+       "23                       1                       False   \n",
+       "8                      0.5                       False   \n",
+       "20                       1                       False   \n",
+       "9                      0.5                       False   \n",
+       "18                       1                       False   \n",
+       "1                      0.5                        True   \n",
+       "4                      0.5                        True   \n",
+       "21                       1                       False   \n",
+       "5                      0.5                        True   \n",
+       "2                      0.5                        True   \n",
+       "17                       1                        True   \n",
+       "14                       1                        True   \n",
+       "13                       1                        True   \n",
+       "0                      0.5                        True   \n",
+       "16                       1                        True   \n",
+       "3                      0.5                        True   \n",
+       "12                       1                        True   \n",
+       "15                       1                        True   \n",
+       "\n",
+       "   param_vectorizer__max_df param_vectorizer__min_df  \\\n",
+       "7                      0.05                       20   \n",
+       "10                      0.1                       20   \n",
+       "19                     0.05                       20   \n",
+       "22                      0.1                       20   \n",
+       "6                      0.05                        5   \n",
+       "11                      0.1                      100   \n",
+       "23                      0.1                      100   \n",
+       "8                      0.05                      100   \n",
+       "20                     0.05                      100   \n",
+       "9                       0.1                        5   \n",
+       "18                     0.05                        5   \n",
+       "1                      0.05                       20   \n",
+       "4                       0.1                       20   \n",
+       "21                      0.1                        5   \n",
+       "5                       0.1                      100   \n",
+       "2                      0.05                      100   \n",
+       "17                      0.1                      100   \n",
+       "14                     0.05                      100   \n",
+       "13                     0.05                       20   \n",
+       "0                      0.05                        5   \n",
+       "16                      0.1                       20   \n",
+       "3                       0.1                        5   \n",
+       "12                     0.05                        5   \n",
+       "15                      0.1                        5   \n",
+       "\n",
+       "                                               params  split0_test_score  \\\n",
+       "7   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.485030   \n",
+       "10  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.482890   \n",
+       "19  {'classifier__alpha': 1, 'classifier__fit_prio...           0.467330   \n",
+       "22  {'classifier__alpha': 1, 'classifier__fit_prio...           0.454830   \n",
+       "6   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.456127   \n",
+       "11  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.438232   \n",
+       "23  {'classifier__alpha': 1, 'classifier__fit_prio...           0.433915   \n",
+       "8   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.436178   \n",
+       "20  {'classifier__alpha': 1, 'classifier__fit_prio...           0.428215   \n",
+       "9   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.430638   \n",
+       "18  {'classifier__alpha': 1, 'classifier__fit_prio...           0.402402   \n",
+       "1   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.389797   \n",
+       "4   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.379323   \n",
+       "21  {'classifier__alpha': 1, 'classifier__fit_prio...           0.366936   \n",
+       "5   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.354850   \n",
+       "2   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.349005   \n",
+       "17  {'classifier__alpha': 1, 'classifier__fit_prio...           0.312332   \n",
+       "14  {'classifier__alpha': 1, 'classifier__fit_prio...           0.304942   \n",
+       "13  {'classifier__alpha': 1, 'classifier__fit_prio...           0.301539   \n",
+       "0   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.295635   \n",
+       "16  {'classifier__alpha': 1, 'classifier__fit_prio...           0.286746   \n",
+       "3   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.276452   \n",
+       "12  {'classifier__alpha': 1, 'classifier__fit_prio...           0.183196   \n",
+       "15  {'classifier__alpha': 1, 'classifier__fit_prio...           0.165277   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
+       "7            0.463849           0.481840         0.476906        0.009324   \n",
+       "10           0.459556           0.479362         0.473936        0.010270   \n",
+       "19           0.442994           0.464302         0.458208        0.010829   \n",
+       "22           0.422902           0.450677         0.442803        0.014174   \n",
+       "6            0.422456           0.443827         0.440803        0.013912   \n",
+       "11           0.440464           0.422667         0.433788        0.007916   \n",
+       "23           0.439470           0.416031         0.429805        0.010001   \n",
+       "8            0.425724           0.418396         0.426766        0.007297   \n",
+       "20           0.425398           0.411051         0.421555        0.007516   \n",
+       "9            0.406619           0.420213         0.419157        0.009834   \n",
+       "18           0.372353           0.386189         0.386981        0.012280   \n",
+       "1            0.372619           0.388358         0.383591        0.007781   \n",
+       "4            0.364667           0.379060         0.374350        0.006848   \n",
+       "21           0.343361           0.363883         0.358060        0.010468   \n",
+       "5            0.349237           0.342737         0.348941        0.004950   \n",
+       "2            0.341832           0.328617         0.339818        0.008444   \n",
+       "17           0.297655           0.307471         0.305819        0.006104   \n",
+       "14           0.296921           0.297121         0.299661        0.003735   \n",
+       "13           0.285396           0.297272         0.294736        0.006830   \n",
+       "0            0.276759           0.296270         0.289555        0.009052   \n",
+       "16           0.272260           0.277084         0.278696        0.006023   \n",
+       "3            0.265509           0.268949         0.270303        0.004569   \n",
+       "12           0.186144           0.180323         0.183221        0.002376   \n",
+       "15           0.165840           0.167088         0.166068        0.000757   \n",
+       "\n",
+       "    rank_test_score  \n",
+       "7                 1  \n",
+       "10                2  \n",
+       "19                3  \n",
+       "22                4  \n",
+       "6                 5  \n",
+       "11                6  \n",
+       "23                7  \n",
+       "8                 8  \n",
+       "20                9  \n",
+       "9                10  \n",
+       "18               11  \n",
+       "1                12  \n",
+       "4                13  \n",
+       "21               14  \n",
+       "5                15  \n",
+       "2                16  \n",
+       "17               17  \n",
+       "14               18  \n",
+       "13               19  \n",
+       "0                20  \n",
+       "16               21  \n",
+       "3                22  \n",
+       "12               23  \n",
+       "15               24  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "optimisation_pipeline = GridSearchCV(\n",
+    "    create_pipeline(),\n",
+    "    {\n",
+    "        \"vectorizer__min_df\": [5, 20, 100],\n",
+    "        \"vectorizer__max_df\": [0.05, 0.1],\n",
+    "        \"classifier__alpha\": [0.5, 1],\n",
+    "        \"classifier__fit_prior\": [True, False],\n",
+    "    },\n",
+    "    scoring=\"f1_macro\",\n",
+    "    cv=3,\n",
+    "    n_jobs=-1,\n",
+    "    verbose=1,\n",
+    ")\n",
+    "optimisation_pipeline.fit(X, y)\n",
+    "\n",
+    "results = pd.DataFrame(optimisation_pipeline.cv_results_)\n",
+    "results.sort_values(\"rank_test_score\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;,\n",
+       "                 TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)),\n",
+       "                (&#x27;classifier&#x27;, MultinomialNB(alpha=0.5, fit_prior=False))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;,\n",
+       "                 TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)),\n",
+       "                (&#x27;classifier&#x27;, MultinomialNB(alpha=0.5, fit_prior=False))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=0.5, fit_prior=False)</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('vectorizer',\n",
+       "                 TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)),\n",
+       "                ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import set_config\n",
+    "\n",
+    "set_config(display=\"diagram\")\n",
+    "\n",
+    "classifier = create_pipeline()\n",
+    "classifier.set_params(**optimisation_pipeline.best_params_)\n",
+    "classifier.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Export the model using GreatAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;5;39m2022-06-25 14:50:53,592 |     INFO | Copying file for small-domain-prediction-0\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,613 |     INFO | Compressing small-domain-prediction-0\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,917 |     INFO | Uploading /tmp/tmpvxez8op8/small-domain-prediction-0.tar.gz to Mongo (GridFS)\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,972 |     INFO | Uploading small-domain-prediction-0.tar.gz 0.26/1.85 MB (14.1%)\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,974 |     INFO | Uploading small-domain-prediction-0.tar.gz 0.52/1.85 MB (28.2%)\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,975 |     INFO | Uploading small-domain-prediction-0.tar.gz 0.78/1.85 MB (42.3%)\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,977 |     INFO | Uploading small-domain-prediction-0.tar.gz 1.04/1.85 MB (56.4%)\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,979 |     INFO | Uploading small-domain-prediction-0.tar.gz 1.31/1.85 MB (70.5%)\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,980 |     INFO | Uploading small-domain-prediction-0.tar.gz 1.57/1.85 MB (84.7%)\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,982 |     INFO | Uploading small-domain-prediction-0.tar.gz 1.83/1.85 MB (98.8%)\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,982 |     INFO | Uploading small-domain-prediction-0.tar.gz 1.85/1.85 MB (100.0%)\u001b[0m\n",
+      "\u001b[38;5;39m2022-06-25 14:50:53,985 |     INFO | Model small-domain-prediction uploaded with version 0\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'small-domain-prediction:0'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from great_ai import save_model\n",
+    "\n",
+    "\n",
+    "save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Next: [Part 3](deploy.ipynb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 ('.env': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "c1f394f9662881005685eeb18d8f9f77079b1b8b9a5ece1f825bfa01fcb7f52f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/hello_world.ipynb b/docs/hello_world.ipynb
index 6e03b24..d3fe2c0 100644
--- a/docs/hello_world.ipynb
+++ b/docs/hello_world.ipynb
@@ -2,24 +2,85 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;5;226m2022-07-08 10:26:12,806 |  WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n",
+      "\u001b[38;5;226m2022-07-08 10:26:12,807 |  WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n",
+      "\u001b[38;5;226m2022-07-08 10:26:12,807 |  WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n",
+      "\u001b[38;5;226m2022-07-08 10:26:12,808 |  WARNING | Cannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n",
+      "\u001b[38;5;39m2022-07-08 10:26:12,808 |     INFO | Settings: configured ✅\u001b[0m\n",
+      "\u001b[38;5;39m2022-07-08 10:26:12,809 |     INFO |   🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n",
+      "\u001b[38;5;39m2022-07-08 10:26:12,809 |     INFO |   🔩 large_file_implementation: LargeFileLocal\u001b[0m\n",
+      "\u001b[38;5;39m2022-07-08 10:26:12,811 |     INFO |   🔩 is_production: False\u001b[0m\n",
+      "\u001b[38;5;39m2022-07-08 10:26:12,811 |     INFO |   🔩 should_log_exception_stack: True\u001b[0m\n",
+      "\u001b[38;5;39m2022-07-08 10:26:12,812 |     INFO |   🔩 prediction_cache_size: 512\u001b[0m\n",
+      "\u001b[38;5;39m2022-07-08 10:26:12,813 |     INFO |   🔩 dashboard_table_size: 20\u001b[0m\n",
+      "\u001b[38;5;226m2022-07-08 10:26:12,813 |  WARNING | You still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
+      "\u001b[38;5;226m2022-07-08 10:26:12,814 |  WARNING | > Find out more at https://se-ml.github.io/practices/\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
     "from great_ai import GreatAI\n",
+    "from asyncio import sleep\n",
     "\n",
     "\n",
     "@GreatAI.create\n",
-    "def hello_world(name: str) -> str:\n",
+    "async def hello_world(name: str) -> str:\n",
+    "    await sleep(1)\n",
     "    return f\"Hello {name}!\""
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Trace(trace_id='27c5e581-ad6e-4d50-bb77-862dfb7963fb', created='2022-07-08T08:26:12.911975', original_execution_time_ms=1.933, logged_values={'arg:name:value': 'hi', 'arg:name:length': 2}, models=[], exception=None, output='Hello hi!', feedback=None, tags=['hello_world', 'online', 'development'])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await hello_world('hi')"
+   ]
   }
  ],
  "metadata": {
-  "language_info": {
-   "name": "python"
+  "kernelspec": {
+   "display_name": "Python 3.10.4 ('.env': venv)",
+   "language": "python",
+   "name": "python3"
   },
-  "orig_nbformat": 4
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "02dd6d3afbfa9fbbe1037d64ad9014965528a1ccad21929d6e72f466389a68ad"
+   }
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2
diff --git a/docs/hello_world.py b/docs/hello_world.py
deleted file mode 100644
index 1f6fd01..0000000
--- a/docs/hello_world.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from great_ai import GreatAI
-
-
-@GreatAI.create
-def hello_world(name: str) -> str:
-    return f"Hello {name}!"
diff --git a/docs/how-to-guides.md b/docs/how-to-guides.md
new file mode 100644
index 0000000..e69de29
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..000ea34
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,17 @@
+# Welcome to MkDocs
+
+For full documentation visit [mkdocs.org](https://www.mkdocs.org).
+
+## Commands
+
+* `mkdocs new [dir-name]` - Create a new project.
+* `mkdocs serve` - Start the live-reloading docs server.
+* `mkdocs build` - Build the documentation site.
+* `mkdocs -h` - Print help message and exit.
+
+## Project layout
+
+    mkdocs.yml    # The configuration file.
+    docs/
+        index.md  # The documentation homepage.
+        ...       # Other markdown pages, images and other files.
diff --git a/docs/map.ipynb b/docs/map.ipynb
deleted file mode 100644
index 9f07b8c..0000000
--- a/docs/map.ipynb
+++ /dev/null
@@ -1,115 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/data/projects/great_ai/.env/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "from great_ai.utilities import parallel_map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[38;5;39m2022-06-29 09:11:18,070 |     INFO | Parallel map: configured ✅\u001b[0m\n",
-      "\u001b[38;5;39m2022-06-29 09:11:18,071 |     INFO |   ⚙️  concurrency: 200\u001b[0m\n",
-      "\u001b[38;5;39m2022-06-29 09:11:18,071 |     INFO |   ⚙️  chunk length: 1\u001b[0m\n",
-      "\u001b[38;5;39m2022-06-29 09:11:18,071 |     INFO |   ⚙️  chunk count: 1000\u001b[0m\n",
-      "\u001b[38;5;39m2022-06-29 09:11:18,072 |     INFO |   ⚙️  function size: 0 kB\u001b[0m\n",
-      "Parallel map: 100%|██████████| 1000/1000 [00:00<00:00, 1345.90it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from time import sleep\n",
-    "\n",
-    "parallel_map(lambda x: sleep(0.1), range(int(1000)), concurrency=200)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[38;5;39m2022-06-29 09:11:20,399 |     INFO | Parallel map: configured ✅\u001b[0m\n",
-      "\u001b[38;5;39m2022-06-29 09:11:20,401 |     INFO |   ⚙️  concurrency: 12\u001b[0m\n",
-      "\u001b[38;5;39m2022-06-29 09:11:20,401 |     INFO |   ⚙️  chunk length: 1\u001b[0m\n",
-      "\u001b[38;5;39m2022-06-29 09:11:20,402 |     INFO |   ⚙️  chunk count: unknown\u001b[0m\n",
-      "\u001b[38;5;39m2022-06-29 09:11:20,403 |     INFO |   ⚙️  function size: 0 kB\u001b[0m\n",
-      "Parallel map: 10it [00:02,  4.93it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 8, 27, 64, 125, 216, 343, 512, 729]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from time import sleep\n",
-    "\n",
-    "\n",
-    "def my_generator():\n",
-    "    for i in range(10):\n",
-    "        yield i\n",
-    "        sleep(0.2)\n",
-    "\n",
-    "\n",
-    "parallel_map(lambda x: x**3, my_generator(), chunk_length=1)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.4 ('.env': venv)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "d88b0dd276e3f918f7798b7e97af2e3c2f843817b9e5b55a9df0a682ffd80f44"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/open_s3.md b/docs/open_s3.md
index 04708e7..0bef8df 100644
--- a/docs/open_s3.md
+++ b/docs/open_s3.md
@@ -15,7 +15,7 @@ pip install open-large
 ### Simple example
 
 ```python
-from large_file import LargeFileS3
+from great_ai.large_file import LargeFileS3
 
 LargeFileS3.configure_credentials({
     "aws_region_name": "your_region_like_eu-west-2",
diff --git a/docs/overrides/main.html b/docs/overrides/main.html
new file mode 100644
index 0000000..702c96b
--- /dev/null
+++ b/docs/overrides/main.html
@@ -0,0 +1,11 @@
+{% extends "base.html" %}
+
+{% block content %}
+{% if page.nb_url %}
+    <a href="{{ page.nb_url }}" title="Download Notebook" class="md-content__button md-icon">
+        {% include ".icons/material/download.svg" %}
+    </a>
+{% endif %}
+
+{{ super() }}
+{% endblock content %}
diff --git a/docs/reference.md b/docs/reference.md
new file mode 100644
index 0000000..168b950
--- /dev/null
+++ b/docs/reference.md
@@ -0,0 +1 @@
+::: great_ai.utilities.unique
\ No newline at end of file
diff --git a/docs/remote-test/remote.ipynb b/docs/remote-test/remote.ipynb
deleted file mode 100644
index 90d509c..0000000
--- a/docs/remote-test/remote.ipynb
+++ /dev/null
@@ -1,90 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Trace(trace_id='07719c28-e248-43f8-a652-608c49ad5a17', created='2022-06-26T07:55:30.587753', original_execution_time_ms=178.268, logged_values={'arg:text:value': 'I love chemical compounds and nuclear fission.', 'arg:text:length': 46, 'arg:target_confidence:value': 50}, models=[Model(key='small-domain-prediction', version=0)], exception=None, output={'labels': [{'label': 'Physics', 'confidence': 28.0, 'explanation': ['nuclear', 'chemical', 'fission', 'compounds', 'love']}, {'label': 'Chemistry', 'confidence': 22.0, 'explanation': ['chemical', 'compounds', 'nuclear', 'fission', 'love']}]}, feedback=None, tags=['predict_domain', 'online', 'development'])"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from great_ai import call_remote_great_ai_async\n",
-    "\n",
-    "\n",
-    "await call_remote_great_ai_async(\n",
-    "    \"http://localhost:6060\", {\"text\": \"I love chemical compounds and nuclear fission.\"}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<_UnixSelectorEventLoop running=True closed=False debug=False>\n"
-     ]
-    },
-    {
-     "ename": "Exception",
-     "evalue": "Already running in an event loop, you have to call call_remote_great_ai_async.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m/data/projects/great_ai/docs/remote-test/remote.ipynb Cell 1'\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/data/projects/great_ai/docs/remote-test/remote.ipynb#ch0000000?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mgreat_ai\u001b[39;00m \u001b[39mimport\u001b[39;00m call_remote_great_ai\n\u001b[0;32m----> <a href='vscode-notebook-cell:/data/projects/great_ai/docs/remote-test/remote.ipynb#ch0000000?line=3'>4</a>\u001b[0m call_remote_great_ai(\u001b[39m'\u001b[39;49m\u001b[39mhttp://localhost:6060\u001b[39;49m\u001b[39m'\u001b[39;49m, {\n\u001b[1;32m      <a href='vscode-notebook-cell:/data/projects/great_ai/docs/remote-test/remote.ipynb#ch0000000?line=4'>5</a>\u001b[0m     \u001b[39m'\u001b[39;49m\u001b[39mtext\u001b[39;49m\u001b[39m'\u001b[39;49m: \u001b[39m'\u001b[39;49m\u001b[39mI love chemical compounds and nuclear fission.\u001b[39;49m\u001b[39m'\u001b[39;49m\n\u001b[1;32m      <a href='vscode-notebook-cell:/data/projects/great_ai/docs/remote-test/remote.ipynb#ch0000000?line=5'>6</a>\u001b[0m })\n",
-      "File \u001b[0;32m/data/projects/great_ai/src/great_ai/great_ai/remote/call_remote_great_ai.py:17\u001b[0m, in \u001b[0;36mcall_remote_great_ai\u001b[0;34m(base_uri, data, retry_count)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     16\u001b[0m     \u001b[39mprint\u001b[39m(asyncio\u001b[39m.\u001b[39mget_running_loop())\n\u001b[0;32m---> 17\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mException\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mAlready running in an event loop, you have to call \u001b[39m\u001b[39m{\u001b[39;00mcall_remote_great_ai_async\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m     18\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     \u001b[39mpass\u001b[39;00m\n",
-      "\u001b[0;31mException\u001b[0m: Already running in an event loop, you have to call call_remote_great_ai_async."
-     ]
-    }
-   ],
-   "source": [
-    "from great_ai import call_remote_great_ai\n",
-    "\n",
-    "\n",
-    "call_remote_great_ai(\n",
-    "    \"http://localhost:6060\", {\"text\": \"I love chemical compounds and nuclear fission.\"}\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.4 ('.env': venv)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "d88b0dd276e3f918f7798b7e97af2e3c2f843817b9e5b55a9df0a682ffd80f44"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/simple-mag/train.ipynb b/docs/simple-mag/train.ipynb
index 55d403d..cb4f991 100644
--- a/docs/simple-mag/train.ipynb
+++ b/docs/simple-mag/train.ipynb
@@ -19,49 +19,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[38;5;39m2022-06-20 14:33:17,570 |     INFO | Starting parallel map (concurrency: 12, chunk size: 700)\u001b[0m\n"
+      "84000it [00:09, 8647.60it/s] \n",
+      "22399it [00:03, 6368.63it/s]\n"
      ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e6f76bf83615422cb4352dcb3af48a26",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/84000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[38;5;39m2022-06-20 14:33:24,846 |     INFO | Starting parallel map (concurrency: 12, chunk size: 187)\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ab012f9276d74bdda15b00cfec6c8f56",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/22399 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
     "import json\n",
     "from typing import Tuple\n",
     "from great_ai.utilities import clean, parallel_map\n",
+    "from tqdm.cli import tqdm\n",
     "\n",
     "\n",
     "def preprocess(line: str) -> Tuple[str, str]:\n",
@@ -71,14 +38,14 @@
     "\n",
     "\n",
     "with open(\"mag/train.txt\", encoding=\"utf-8\") as f:\n",
-    "    training_data = parallel_map(preprocess, f.readlines())\n",
+    "    training_data = list(tqdm(parallel_map(preprocess, f.readlines())))\n",
     "\n",
     "X_train = [d[0] for d in training_data]\n",
     "y_train = [d[1] for d in training_data]\n",
     "\n",
     "\n",
     "with open(\"mag/test.txt\", encoding=\"utf-8\") as f:\n",
-    "    test_data = parallel_map(preprocess, f.readlines())\n",
+    "    test_data = list(tqdm(parallel_map(preprocess, f.readlines())))\n",
     "\n",
     "X_test = [d[0] for d in test_data]\n",
     "y_test = [d[1] for d in test_data]"
@@ -1070,10 +1037,10 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>1.172014</td>\n",
-       "      <td>0.033318</td>\n",
-       "      <td>0.625773</td>\n",
-       "      <td>0.013720</td>\n",
+       "      <td>1.227354</td>\n",
+       "      <td>0.011710</td>\n",
+       "      <td>0.652844</td>\n",
+       "      <td>0.040511</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1088,10 +1055,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>1.201231</td>\n",
-       "      <td>0.016728</td>\n",
-       "      <td>0.613568</td>\n",
-       "      <td>0.013720</td>\n",
+       "      <td>1.279783</td>\n",
+       "      <td>0.037969</td>\n",
+       "      <td>0.634761</td>\n",
+       "      <td>0.068606</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1106,10 +1073,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>1.211270</td>\n",
-       "      <td>0.036636</td>\n",
-       "      <td>0.670754</td>\n",
-       "      <td>0.076857</td>\n",
+       "      <td>1.332854</td>\n",
+       "      <td>0.171405</td>\n",
+       "      <td>0.743611</td>\n",
+       "      <td>0.109792</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1124,10 +1091,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>1.165763</td>\n",
-       "      <td>0.061206</td>\n",
-       "      <td>0.549646</td>\n",
-       "      <td>0.046776</td>\n",
+       "      <td>1.253222</td>\n",
+       "      <td>0.072134</td>\n",
+       "      <td>0.612344</td>\n",
+       "      <td>0.016771</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1142,10 +1109,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1.277603</td>\n",
-       "      <td>0.127785</td>\n",
-       "      <td>0.650135</td>\n",
-       "      <td>0.023038</td>\n",
+       "      <td>1.472035</td>\n",
+       "      <td>0.080849</td>\n",
+       "      <td>0.654935</td>\n",
+       "      <td>0.044302</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1160,10 +1127,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.118055</td>\n",
-       "      <td>0.058978</td>\n",
-       "      <td>0.622284</td>\n",
-       "      <td>0.014230</td>\n",
+       "      <td>1.380641</td>\n",
+       "      <td>0.054306</td>\n",
+       "      <td>0.739966</td>\n",
+       "      <td>0.053385</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1178,10 +1145,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>1.166192</td>\n",
-       "      <td>0.044232</td>\n",
-       "      <td>0.619351</td>\n",
-       "      <td>0.024824</td>\n",
+       "      <td>1.284987</td>\n",
+       "      <td>0.113903</td>\n",
+       "      <td>0.696876</td>\n",
+       "      <td>0.003757</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1196,10 +1163,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>1.233377</td>\n",
-       "      <td>0.063715</td>\n",
-       "      <td>0.666418</td>\n",
-       "      <td>0.061664</td>\n",
+       "      <td>1.291148</td>\n",
+       "      <td>0.101837</td>\n",
+       "      <td>0.686561</td>\n",
+       "      <td>0.083989</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1214,10 +1181,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>1.245147</td>\n",
-       "      <td>0.089528</td>\n",
-       "      <td>0.645836</td>\n",
-       "      <td>0.006279</td>\n",
+       "      <td>1.268873</td>\n",
+       "      <td>0.042412</td>\n",
+       "      <td>0.645649</td>\n",
+       "      <td>0.022738</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1232,10 +1199,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>1.145362</td>\n",
-       "      <td>0.071149</td>\n",
-       "      <td>0.637540</td>\n",
-       "      <td>0.028854</td>\n",
+       "      <td>1.147120</td>\n",
+       "      <td>0.009335</td>\n",
+       "      <td>0.599006</td>\n",
+       "      <td>0.045637</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1250,10 +1217,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.179663</td>\n",
-       "      <td>0.046808</td>\n",
-       "      <td>0.598699</td>\n",
-       "      <td>0.035973</td>\n",
+       "      <td>1.186988</td>\n",
+       "      <td>0.057330</td>\n",
+       "      <td>0.642233</td>\n",
+       "      <td>0.078248</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1268,10 +1235,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.097773</td>\n",
-       "      <td>0.005871</td>\n",
-       "      <td>0.596514</td>\n",
-       "      <td>0.025899</td>\n",
+       "      <td>1.413231</td>\n",
+       "      <td>0.153288</td>\n",
+       "      <td>0.765960</td>\n",
+       "      <td>0.099535</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1286,10 +1253,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>1.161582</td>\n",
-       "      <td>0.040011</td>\n",
-       "      <td>0.586831</td>\n",
-       "      <td>0.006144</td>\n",
+       "      <td>1.193013</td>\n",
+       "      <td>0.079000</td>\n",
+       "      <td>0.691768</td>\n",
+       "      <td>0.027448</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1304,10 +1271,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
-       "      <td>1.106277</td>\n",
-       "      <td>0.060809</td>\n",
-       "      <td>0.371986</td>\n",
-       "      <td>0.014114</td>\n",
+       "      <td>1.043618</td>\n",
+       "      <td>0.098733</td>\n",
+       "      <td>0.450375</td>\n",
+       "      <td>0.061660</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1322,10 +1289,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>1.217713</td>\n",
-       "      <td>0.044787</td>\n",
-       "      <td>0.587896</td>\n",
-       "      <td>0.024613</td>\n",
+       "      <td>1.301459</td>\n",
+       "      <td>0.062143</td>\n",
+       "      <td>0.660748</td>\n",
+       "      <td>0.056054</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1340,10 +1307,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>1.176654</td>\n",
-       "      <td>0.045971</td>\n",
-       "      <td>0.628005</td>\n",
-       "      <td>0.082375</td>\n",
+       "      <td>1.433934</td>\n",
+       "      <td>0.155240</td>\n",
+       "      <td>0.636608</td>\n",
+       "      <td>0.024064</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1358,10 +1325,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>1.207308</td>\n",
-       "      <td>0.080923</td>\n",
-       "      <td>0.610284</td>\n",
-       "      <td>0.039157</td>\n",
+       "      <td>1.325535</td>\n",
+       "      <td>0.073539</td>\n",
+       "      <td>0.672542</td>\n",
+       "      <td>0.085835</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1376,10 +1343,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>1.201498</td>\n",
-       "      <td>0.035161</td>\n",
-       "      <td>0.574234</td>\n",
-       "      <td>0.019247</td>\n",
+       "      <td>1.237677</td>\n",
+       "      <td>0.070063</td>\n",
+       "      <td>0.651091</td>\n",
+       "      <td>0.102538</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1394,10 +1361,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.157947</td>\n",
-       "      <td>0.096841</td>\n",
-       "      <td>0.586462</td>\n",
-       "      <td>0.010952</td>\n",
+       "      <td>1.394873</td>\n",
+       "      <td>0.105286</td>\n",
+       "      <td>0.637073</td>\n",
+       "      <td>0.041708</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1412,10 +1379,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>1.150511</td>\n",
-       "      <td>0.027807</td>\n",
-       "      <td>0.562870</td>\n",
-       "      <td>0.013724</td>\n",
+       "      <td>1.270732</td>\n",
+       "      <td>0.013414</td>\n",
+       "      <td>0.620984</td>\n",
+       "      <td>0.025393</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1430,10 +1397,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>1.237030</td>\n",
-       "      <td>0.006805</td>\n",
-       "      <td>0.529591</td>\n",
-       "      <td>0.064449</td>\n",
+       "      <td>1.177219</td>\n",
+       "      <td>0.039633</td>\n",
+       "      <td>0.586308</td>\n",
+       "      <td>0.044247</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1448,10 +1415,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>1.103937</td>\n",
-       "      <td>0.046513</td>\n",
-       "      <td>0.569367</td>\n",
-       "      <td>0.011001</td>\n",
+       "      <td>1.165583</td>\n",
+       "      <td>0.046999</td>\n",
+       "      <td>0.603675</td>\n",
+       "      <td>0.031774</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1466,10 +1433,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
-       "      <td>1.080252</td>\n",
-       "      <td>0.040597</td>\n",
-       "      <td>0.326627</td>\n",
-       "      <td>0.002905</td>\n",
+       "      <td>0.907388</td>\n",
+       "      <td>0.121543</td>\n",
+       "      <td>0.354655</td>\n",
+       "      <td>0.023775</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1484,10 +1451,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>1.275601</td>\n",
-       "      <td>0.087971</td>\n",
-       "      <td>0.580025</td>\n",
-       "      <td>0.034914</td>\n",
+       "      <td>1.210845</td>\n",
+       "      <td>0.041339</td>\n",
+       "      <td>0.653606</td>\n",
+       "      <td>0.028117</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1506,30 +1473,30 @@
       ],
       "text/plain": [
        "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
-       "12       1.172014      0.033318         0.625773        0.013720   \n",
-       "15       1.201231      0.016728         0.613568        0.013720   \n",
-       "18       1.211270      0.036636         0.670754        0.076857   \n",
-       "21       1.165763      0.061206         0.549646        0.046776   \n",
-       "3        1.277603      0.127785         0.650135        0.023038   \n",
-       "0        1.118055      0.058978         0.622284        0.014230   \n",
-       "9        1.166192      0.044232         0.619351        0.024824   \n",
-       "6        1.233377      0.063715         0.666418        0.061664   \n",
-       "13       1.245147      0.089528         0.645836        0.006279   \n",
-       "16       1.145362      0.071149         0.637540        0.028854   \n",
-       "4        1.179663      0.046808         0.598699        0.035973   \n",
-       "1        1.097773      0.005871         0.596514        0.025899   \n",
-       "19       1.161582      0.040011         0.586831        0.006144   \n",
-       "22       1.106277      0.060809         0.371986        0.014114   \n",
-       "7        1.217713      0.044787         0.587896        0.024613   \n",
-       "10       1.176654      0.045971         0.628005        0.082375   \n",
-       "14       1.207308      0.080923         0.610284        0.039157   \n",
-       "17       1.201498      0.035161         0.574234        0.019247   \n",
-       "2        1.157947      0.096841         0.586462        0.010952   \n",
-       "5        1.150511      0.027807         0.562870        0.013724   \n",
-       "20       1.237030      0.006805         0.529591        0.064449   \n",
-       "8        1.103937      0.046513         0.569367        0.011001   \n",
-       "23       1.080252      0.040597         0.326627        0.002905   \n",
-       "11       1.275601      0.087971         0.580025        0.034914   \n",
+       "12       1.227354      0.011710         0.652844        0.040511   \n",
+       "15       1.279783      0.037969         0.634761        0.068606   \n",
+       "18       1.332854      0.171405         0.743611        0.109792   \n",
+       "21       1.253222      0.072134         0.612344        0.016771   \n",
+       "3        1.472035      0.080849         0.654935        0.044302   \n",
+       "0        1.380641      0.054306         0.739966        0.053385   \n",
+       "9        1.284987      0.113903         0.696876        0.003757   \n",
+       "6        1.291148      0.101837         0.686561        0.083989   \n",
+       "13       1.268873      0.042412         0.645649        0.022738   \n",
+       "16       1.147120      0.009335         0.599006        0.045637   \n",
+       "4        1.186988      0.057330         0.642233        0.078248   \n",
+       "1        1.413231      0.153288         0.765960        0.099535   \n",
+       "19       1.193013      0.079000         0.691768        0.027448   \n",
+       "22       1.043618      0.098733         0.450375        0.061660   \n",
+       "7        1.301459      0.062143         0.660748        0.056054   \n",
+       "10       1.433934      0.155240         0.636608        0.024064   \n",
+       "14       1.325535      0.073539         0.672542        0.085835   \n",
+       "17       1.237677      0.070063         0.651091        0.102538   \n",
+       "2        1.394873      0.105286         0.637073        0.041708   \n",
+       "5        1.270732      0.013414         0.620984        0.025393   \n",
+       "20       1.177219      0.039633         0.586308        0.044247   \n",
+       "8        1.165583      0.046999         0.603675        0.031774   \n",
+       "23       0.907388      0.121543         0.354655        0.023775   \n",
+       "11       1.210845      0.041339         0.653606        0.028117   \n",
        "\n",
        "   param_classifier__alpha param_classifier__fit_prior  \\\n",
        "12                       1                        True   \n",
@@ -1726,7 +1693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
diff --git a/docs/tutorials.md b/docs/tutorials.md
new file mode 100644
index 0000000..e69de29
diff --git a/mkdocs.yml b/mkdocs.yml
index 3dd976f..981e168 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,10 +1,32 @@
 site_name: GreatAI documentation
 
+repo_url: https://github.com/ScoutinScience/great-ai
+edit_uri: edit/main/docs/
+
 theme:
   name: "material"
+  custom_dir: docs/overrides
+  palette:
+    - scheme: default
+      toggle:
+        icon: material/brightness-7 
+        name: Switch to dark mode
+
+    - scheme: slate
+      toggle:
+        icon: material/brightness-4
+        name: Switch to light mode
 
 plugins:
+  - git-revision-date-localized:
   - mkdocstrings
+  - search
+  - mkdocs-jupyter:
+      include_source: true
+      # allow_errors: false
+      # execute: false
+      # execute_ignore: "my-secret-files/*.ipynb"
+      # theme: dark
 
 nav:
   - Home: index.md
@@ -12,3 +34,4 @@ nav:
   - How-To Guides: how-to-guides.md
   - reference.md
   - explanation.md
+  - Notebook page: simple-mag/train.ipynb
diff --git a/pyproject.toml b/pyproject.toml
index f321ccf..ec2b7f7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -50,6 +50,7 @@ dev = [
     "mkdocstrings[python]",
     "mkdocs-material",
     "mkdocs-jupyter",
+    "mkdocs-git-revision-date-localized-plugin",
     "autoflake",
     "isort",
     "black[jupyter]",
@@ -59,6 +60,7 @@ dev = [
     "pytest-cov",
     "pytest-subtests",
     "pytest-asyncio",
+    'tqdm',
 ]
 
 [project.urls]