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+# Train and deploy a SOTA model
+
+## Overview
+
+You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the [SciBERT paper](https://arxiv.org/abs/1903.10676) in which SciBERT [achieved an F1-score of 0.6571](https://paperswithcode.com/sota/sentence-classification-on-paper-field). We are going to outperform it using a trivial text classification model: a [Linear SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html).
+
+We use the same synthetic dataset derived from the [Microsoft Academic Graph](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/). The dataset is [available here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag).
+
+## Objectives
+
+1. You will see how the `great_ai.utilities` can integrate into your Data Science workflow.
+2. You will use `great_ai.large_file` to version and store your trained model
+3. You will use `GreatAI` to prepare your model for a robust and responsible deployment
+
+!!! success
+    You are ready to start the tutorial. Feel free to come back to the [summary](#summary) section once you're finished.
+
+<div style="display: flex; justify-content: space-evenly;" markdown>
+[:fontawesome-solid-chart-simple: Train it](train.ipynb){ .md-button .md-button--primary }
+
+[:material-cloud-tags: Deploy it](deploy.ipynb){ .md-button .md-button--secondary }
+</div>
+
+
+## Summary
+
+### The [training notebook](train.ipynb)
+
+We load and preprocess the dataset while relying on `great_ai.utilities.clean` for the heavy-lifting. Additionally, the preprocessing is parallelised using `great_ai.utilities.simple_parallel_map`.
+
+After training and evaluating a model, it is exported using `great_ai.save_model`.
+
+??? tip "Using the cloud"
+    To store your model remotely, you need to set your credentials before calling `save_model`.
+
+    For example, to use [AWS S3](https://aws.amazon.com/s3/):
+    ```python
+    from great_ai.large_file import LargeFileS3
+
+    LargeFileS3.configure(
+        aws_region_name='eu-west-2',
+        aws_access_key_id='MY_AWS_ACCESS_KEY',
+        aws_secret_access_key='MY_AWS_SECRET_KEY',
+        large_files_bucket_name='my_bucket_for_models'
+    )
+
+    from great_ai import save_model
+
+    save_model(model, key='my-domain-predictor')
+    ```
+
+### The [deployment notebook](deploy.ipynb)
+
+We create an inference function that can be hardened by wrapping it in a `great_ai.GreatAI` instance.
+
+```python
+from great_ai import GreatAI, use_model
+from great_ai.utilities import clean
+
+@GreatAI.create
+@use_model('my-domain-predictor')   #(1)
+def predict_domain(sentence, model):
+    inputs = [clean(sentence)]
+    return str(model.predict(inputs)[0])
+```
+
+1.  `@use_model` loads and injects your model into the `predict_domain` function's `model` argument.
+    You can freely reference it knowing that it is always given to the function.
+
+Finally, we deploy the model, inference function, and the GreatAI wrapping all of these. For that we either use: `great-ai deploy.ipynb` or build a Docker image.