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# Train and deploy a SOTA model
Let's see `great-ai` in action by going over the life-cycle of a simple service.
Let's see `great-ai` in action by going over the lifecycle of a simple service.
## Objectives
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## 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){ target=_blank } in which SciBERT [achieved an F1-score of 0.6571](https://paperswithcode.com/sota/sentence-classification-on-paper-field){ target=_blank }. 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){ target=_blank }.
You will 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){ target=_blank } in which SciBERT [achieved an F1-score of 0.6571](https://paperswithcode.com/sota/sentence-classification-on-paper-field){ target=_blank }. 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){ target=_blank }.
We use the same synthetic dataset derived from the [Microsoft Academic Graph](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/){ target=_blank }. The dataset is [available here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag){ target=_blank }.
!!! success
You are ready to start the tutorial. Feel free to come back to the [summary](#summary) section once you're finished.
You are ready to start the tutorial. Feel free to return 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 }
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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`.
To store your model remotely, you must set your credentials before calling `save_model`.
For example, to use [AWS S3](https://aws.amazon.com/s3){ target=_blank }:
```python
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```
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 the function is always provided with it.
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.](/how-to-guides/use-service)