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In this liveProject, you’ll train your model and build a scoring pipeline using an ML feature store. You’ll explore a sample data set for diagnosing diabetes, generate new features and store them in a feature store, train and retrain ML models, and build a scoring process. You’ll employ common feature engineering techniques to train the model, then test and retrain it as needed. You’ll also work on setting up a scoring pipeline, and brainstorm ML development using a feature store. In this project, you will learn how to store the features for a machine learning model so they can be reused in other machine learning projects.
This liveProject is for data scientists and engineers who are familiar with Python, machine learning, and data modeling. To begin this liveProject you will need to be familiar with the following:
In this liveProject, you’ll use common Python libraries for the important task of machine learning development using an ML feature store.
geekle is based on a wordle clone.