Book Image

Feature Store for Machine Learning

By : Jayanth Kumar M J
Book Image

Feature Store for Machine Learning

By: Jayanth Kumar M J

Overview of this book

Feature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed and curated features for ML models. This makes them available for model training, inference (batch and online), and reuse in other ML pipelines. Knowing how to utilize feature stores to their fullest potential can save you a lot of time and effort, and this book will teach you everything you need to know to get started. Feature Store for Machine Learning is for data scientists who want to learn how to use feature stores to share and reuse each other's work and expertise. You’ll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model. While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work. With a hands-on approach to implementation and associated methodologies, you'll get up and running in no time. By the end of this book, you’ll have understood why feature stores are essential and how to use them in your ML projects, both on your local system and on the cloud.
Table of Contents (13 chapters)
1
Section 1 – Why Do We Need a Feature Store?
4
Section 2 – A Feature Store in Action
9
Section 3 – Alternatives, Best Practices, and a Use Case

Model packaging

In the previous section, we built two versions of the model. In this section, let's package one of the models and save it for model scoring and deployment. As mentioned in the previous section, let's package the XGBClassifier model. Again, for packaging, there are different solutions and tools available. To avoid setting up another tool, I will be using the joblib library to package the model:

  1. Continuing in the same notebook that produced the XGBClassifier model, the following code block installs the joblib library:
    #install job lib library for model packaging
    !pip install joblib
  2. After installing the joblib library, the next step is to package the model object using it. The following code block packages the model and writes the model to a specific location on the filesystem:
    import joblib
    joblib.dump(xgb_model, '/content/customer_segment-v0.0')

The preceding code block creates a file in the /content folder. To...