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Machine Learning Engineering with Python

You're reading from Machine Learning Engineering with Python Manage the production life cycle of machine learning models using MLOps with practical examples

Product type Paperback

Published in Nov 2021

Publisher Packt

ISBN-13 9781801079259

Length 276 pages

Edition 1st Edition

Languages

Python

Tools

GitHub

Concepts

Machine Learning

Author (1):

Andrew P. McMahon

View More author details

Table of Contents (13) Chapters

Preface

1. Section 1: What Is ML Engineering?

2. Chapter 1: Introduction to ML Engineering FREE CHAPTER

3. Chapter 2: The Machine Learning Development Process

4. Section 2: ML Development and Deployment

5. Chapter 3: From Model to Model Factory

6. Chapter 4: Packaging Up

7. Chapter 5: Deployment Patterns and Tools

8. Chapter 6: Scaling Up

9. Section 3: End-to-End Examples

10. Chapter 7: Building an Example ML Microservice

11. Chapter 8: Building an Extract Transform Machine Learning Use Case

12. Other Books You May Enjoy

Technical requirements

To complete this chapter, you will need to have installed the following Python packages and tools:

MLflow version >= 1.15.0 (the Model Registry examples require later versions of MLflow)
TensorFlow
auto-keras
Hyperopt
Optuna
auto-sklearn
alibi-detect

The rest of the chapter is locked

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Authors (1)

Andrew P. McMahon

Andrew P. McMahon

Andrew P. McMahon has spent years building high-impact ML products across a variety of industries. He is currently Head of MLOps for NatWest Group in the UK and has a PhD in theoretical condensed matter physics from Imperial College London. He is an active blogger, speaker, podcast guest, and leading voice in the MLOps community. He is co-host of the AI Right podcast and was named ‘Rising Star of the Year' at the 2022 British Data Awards and ‘Data Scientist of the Year' by the Data Science Foundation in 2019.

See other products by Andrew P. McMahon

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