Deployed b1a66cb with MkDocs version: 1.3.0

tutorial/index.md

@@ -1,11 +1,11 @@
 # Train and deploy a SOTA model
 
-Let's see GreatAI in action by going over the life-cycle of a simple service.
+Let's see `great-ai` in action by going over the life-cycle of a simple service.
 
 ## 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.
+1. You will see how the [great_ai.utilities](/reference/utilities) can integrate into your Data Science workflow.
+2. You will use [great_ai.large_file](/reference/large_file) to version and store your trained model.
 3. You will use [GreatAI][great_ai.GreatAI] to prepare your model for a robust and responsible deployment.
 
 ## Overview
@@ -14,7 +14,6 @@ You are going to train a field of study (domain) classifier for scientific sente
 
 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).
 
-
 !!! success
     You are ready to start the tutorial. Feel free to come back to the [summary](#summary) section once you're finished.
 
@@ -24,19 +23,18 @@ We use the same synthetic dataset derived from the [Microsoft Academic Graph](ht
 [:material-cloud-tags: Deploy it](deploy.ipynb){ .md-button .md-button--primary }
 </div>
 
-
 ## Summary
 
-### The [training notebook](train.ipynb)
+### [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][]
+We load and preprocess the dataset while relying on [great_ai.utilities.clean][great_ai.utilities.clean.clean] for doing 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 "Remote storage"
     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/):
+    For example, to use [AWS S3](https://aws.amazon.com/s3){ target=_blank }:
     ```python
     from great_ai.large_file import LargeFileS3
 
@@ -52,7 +50,9 @@ After training and evaluating a model, it is exported using [great_ai.save_model
     save_model(model, key='my-domain-predictor')
     ```
 
-### The [deployment notebook](deploy.ipynb)
+    For more info, checkout [the configuration how-to page](/how-to-guides/configure-service).
+
+### [Deployment notebook](deploy.ipynb)
 
 We create an inference function that can be hardened by wrapping it in a [GreatAI][great_ai.GreatAI] instance.
 
@@ -68,6 +68,12 @@ def predict_domain(sentence, model):
 ```
 
 1.  [@use_model][great_ai.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.
+    You can freely reference it knowing that the function is always provided with it.
 
-Finally, we test the model's inference function through the GreatAI dashboard. [The only thing left is to deploy the hardened-service.](/how-to-guides/use-service)
+Finally, we test the model's inference function through the GreatAI dashboard. [The only thing left is to deploy the hardened-service properly.](/how-to-guides/use-service)
+
+<div style="display: flex; justify-content: center;" markdown>  
+[:material-book: Learn about all the features](/how-to-guides/create-service){ .md-button .md-button--primary }
+
+[:material-test-tube: Look at more examples](/examples/simple/data){ .md-button .md-button--secondary }
+</div>