Summary
In this chapter, we started with the goal of adding the Feast feature store to our ML model development. We accomplished that by creating the required resources on AWS, adding an IAM user to access those resources. After creating the resources, we went through the steps of the ML life cycle again from the problem statement to feature engineering and feature ingestion. We also verified that created feature definitions and ingested data could be queried through the API.
Now that we have set the stage for the next steps of the ML life cycle – model training, validation, deployment, and scoring, in the next chapter, we will learn how the addition of the feature store right from the beginning makes the model production-ready when the development is complete.