Code: USD75OFF
This is the code repository for Feature Store for Machine Learning , published by Packt.
Curate, discover, share and serve ML features at scale
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.
This book covers the following exciting features:
- Understand the significance of feature stores in a machine learning pipeline
- Become well-versed with how to curate, store, share and discover features using feature stores
- Explore the different components and capabilities of a feature store
- Discover how to use feature stores with batch and online models
- Accelerate your model life cycle and reduce costs
- Deploy your first feature store for production use cases
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
import boto3
import awswrangler as wr
from urllib.parse import unquote_plus
Following is what you need for this book: If you have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using them, then this book is for you. Data/machine learning engineers and data scientists who build machine learning models for production systems in any domain, those supporting data engineers in productionizing ML models, and platform engineers who build data science (ML) platforms for the organization will also find plenty of practical advice in the later chapters of this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-13).
| Chapter | Software required | OS required |
|---|---|---|
| 1-13 | Python 3.7 | Any OS |
| 1-13 | Jupyter notebook environment | Any OS |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Jayanth Kumar M J is a Lead Data engineer at Cimpress USA. He specializes in building platform components for data scientists and data engineers to make MLOps smooth and self-service. He is also a Feast feature store contributor.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.
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