Initial commit for customizing ads deployment and changes for your-fi…#2787
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Initial commit for customizing ads deployment and changes for your-fi…#2787
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…rst-data science-project
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Initial commit for customizing ADS deployment and updates to your-first-data-science-project.
This section provides an end-to-end example of how to customize the default model artifact files (score.py and runtime.yaml) generated by the standard ADS deployment. The example covers the entire workflow (data processing and building an sklearn pipeline to inference). It also highlights several tricky aspects, such as using a published conda environment instead of the default conda slug.
This update extends the guide to include instructions for creating a log group and log. It also adds additional policies and matching rules, making the document a more comprehensive starting point for various data science projects (not just notebooks and deployments).