ML Feature Engineering

Train and Score with Feature Store you own this product

This project is part of the liveProject series ML Feature Engineering and Modeling using Python
intermediate Python and scikit-learn • basics of Jupyter Notebook, pandas, and SQL
skills learned
generate new features and store them in a feature store • train and retrain ML models • build a scoring process
Jayesh Patel
1 week · 4-6 hours per week · BEGINNER

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Look inside

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 project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Jayesh Patel
Jayesh Patel is a strategic big data leader and proven architect who successfully designed complex data processes, architected machine learning pipelines, and developed big data analytics solutions over the past 15+ years. He currently works for Rockstar Games, architecting data-driven big data platforms and artificial intelligence solutions to keep players engaged in Red Dead Redemption II and Grand Theft Auto V. He is an active senior member of the IEEE. His expertise and research in the big data space are well received in numerous international IEEE conferences. He is an editorial board member of a renowned international journal. He actively guides and reviews the research work of other scholars and professors around the world. He completed his master’s from San Diego State University in 2009.


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:

  • Intermediate Python
  • Basics of Jupyter Notebook
  • Basics of pandas
  • Intermediate scikit-learn
  • Basics of SQL
  • Basic file processing
  • Intermediate data processing and feature engineering
  • Intermediate machine learning pipelines
  • Basic understanding of ML development cycles
  • Basic understanding of classification with linear regression
  • Basic understanding of ML feature stores

you will learn

In this liveProject, you’ll use common Python libraries for the important task of machine learning development using an ML feature store.

  • Classification with logistic regression
  • Generating model pickle files with Joblib
  • Generating new features and make your features reusable using an ML feature store
  • ML scoring pipeline with ML feature stores


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
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While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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