Train and deploy a SOTA model

Let's see great-ai in action by going over the life-cycle of a simple service.

Objectives

You will see how the great_ai.utilities can integrate into your Data Science workflow.
You will use great_ai.large_file to version and store your trained model.
You will use [GreatAI][great_ai.GreatAI] to prepare your model for a robust and responsible deployment.

Overview

You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571. We are going to outperform it using a trivial text classification model: a Linear SVM.

We use the same synthetic dataset derived from the Microsoft Academic Graph. The dataset is available here.

!!! success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished.

[:fontawesome-solid-chart-simple: Train it](train.ipynb){ .md-button .md-button--primary }

:material-cloud-tags: Deploy it{ .md-button .md-button--primary }

Summary

The training notebook

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){ target=_blank }:
```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')
```

For more info, checkout [the configuration how-to page](/how-to-guides/configure-service).

The deployment notebook

We create an inference function that can be hardened by wrapping it in a [GreatAI][great_ai.GreatAI] instance.

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])

[@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 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 properly.

[:material-book: Learn about more features](/how-to-guides/create-service){ .md-button .md-button--primary }

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