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Minor fixes

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Andras Schmelczer 2022-07-12 19:17:10 +02:00
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@ -25,7 +25,7 @@ We use the same synthetic dataset derived from the [Microsoft Academic Graph](ht
## Summary
### The [training notebook](train.ipynb)
### [Training notebook](train.ipynb)
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][]
@ -52,7 +52,7 @@ After training and evaluating a model, it is exported using [great_ai.save_model
For more info, checkout [the configuration how-to page](/how-to-guides/configure-service).
### The [deployment notebook](deploy.ipynb)
### [Deployment notebook](deploy.ipynb)
We create an inference function that can be hardened by wrapping it in a [GreatAI][great_ai.GreatAI] instance.
@ -73,5 +73,7 @@ def predict_domain(sentence, model):
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 more features](/how-to-guides/create-service){ .md-button .md-button--primary }
[: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>