In this part, we:
- Train a tabular classification model.
- Upload the model and datasets to the Openlayer platform to start evaluating them.
Here is a link to a Colab notebook where you’ll have everything you need to follow this walkthrough.
Train the model
The first part of the notebook looks like a standard training pipeline.
It contains all the code to load the dataset, apply a one-hot encoding to the categorical features, and train a gradient-boosting classifier. We added comments to guide you along the way.
Run the notebook cells up to the point where we evaluate the model’s performance.
How is our model doing? Do you see the accuracy and F1? Despite their popularity, aggregate metrics, such as accuracy, can be very misleading.
Upload the model and datasets
We use the
openlayer Python client to interact with the Openlayer platform.
That’s the role of the second part of the notebook. It demonstrates how to use the client API to upload our model and datasets.
Run the second part of the notebook.
Don’t forget to replace the
YOUR_API_KEY_HERE in the cell that instantiates the Openlayer Client with your API key (which you can find in the platform’s “Workspace settings” — or follow the Find your API key guide.)
You refer to the API reference to learn more about the Python client API.
With our model and datasets on the platform, we are good to move to the next part of the tutorial!