You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800562882

Length 370 pages

Edition 1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Emmanuel Raj

View More author details

Table of Contents (18) Chapters

Preface

1. Section 1: Framework for Building Machine Learning Models

2. Chapter 1: Fundamentals of an MLOps Workflow FREE CHAPTER

3. Chapter 2: Characterizing Your Machine Learning Problem

4. Chapter 3: Code Meets Data

5. Chapter 4: Machine Learning Pipelines

6. Chapter 5: Model Evaluation and Packaging

7. Section 2: Deploying Machine Learning Models at Scale

8. Chapter 6: Key Principles for Deploying Your ML System

9. Chapter 7: Building Robust CI/CD Pipelines

10. Chapter 8: APIs and Microservice Management

11. Chapter 9: Testing and Securing Your ML Solution

12. Chapter 10: Essentials of Production Release

13. Section 3: Monitoring Machine Learning Models in Production

14. Chapter 11: Key Principles for Monitoring Your ML System

15. Chapter 12: Model Serving and Monitoring

16. Chapter 13: Governing the ML System for Continual Learning

17. Other Books You May Enjoy

Toward the ML Pipeline

So far, we have processed the data by working on irregularities such as missing data, selected features by observing correlations, created new features, and finally ingested and versioned the processed data to the Machine learning workspace. There are two ways to fuel the data ingestion for ML model training in the ML pipeline. One way is from the central storage (where all your raw data is stored) and the second way is using a feature store. As knowledge is power, Let's get to know the use of the feature store before we move to the ML pipeline.

Feature Store

A feature store compliments the central storage by storing important features and make them available for training or inference. A feature store is a store where you transform raw data into useful features that ML models can use directly to train and infer to make predictions. Raw Data typically comes from various data sources, which are structured, unstructured, streaming, batch, and real-time...