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

Product type Paperback

Published in Aug 2023

Publisher Packt

ISBN-13 9781837631964

Length 462 pages

Edition 2nd Edition

Languages

Python

Tools

GitHub

Concepts

Machine Learning

Author (1):

Andrew P. McMahon

View More author details

Table of Contents (12) Chapters

Preface

Who this book is for
What this book covers
To get the most out of this book
Get in touch

1. Introduction to ML Engineering

Technical requirements
Defining a taxonomy of data disciplines
Working as an effective team
ML engineering in the real world
What does an ML solution look like?
High-level ML system design
Summary

2. The Machine Learning Development Process FREE CHAPTER

Technical requirements
Setting up our tools
Concept to solution in four steps
Summary

3. From Model to Model Factory

Technical requirements
Defining the model factory
Learning about learning
Engineering features for machine learning
Designing your training system
Retraining required
Persisting your models
Building the model factory with pipelines
Summary

4. Packaging Up

Technical requirements
Writing good Python
Choosing a style
Packaging your code
Building your package
Testing, logging, securing, and error handling
Not reinventing the wheel
Summary

5. Deployment Patterns and Tools

Technical requirements
Architecting systems
Exploring some standard ML patterns
Containerizing
Hosting your own microservice on AWS
Building general pipelines with Airflow
Building advanced ML pipelines
Selecting your deployment strategy
Summary

6. Scaling Up

Technical requirements
Scaling with Spark
Spinning up serverless infrastructure
Containerizing at scale with Kubernetes
Scaling with Ray
Designing systems at scale
Summary

7. Deep Learning, Generative AI, and LLMOps

Going deep with deep learning
Living it large with LLMs
Building the future with LLMOps
Summary

8. Building an Example ML Microservice

Technical requirements
Understanding the forecasting problem
Designing our forecasting service
Selecting the tools
Training at scale
Serving the models with FastAPI
Containerizing and deploying to Kubernetes
Summary

9. Building an Extract, Transform, Machine Learning Use Case

Technical requirements
Understanding the batch processing problem
Designing an ETML solution
Selecting the tools
Executing the build
Summary

10. Other Books You May Enjoy

11. Index

Technical requirements

To work through the examples in this chapter, we require the following tools to be installed:

AWS CLI v2
Postman
Docker

The rest of the chapter is locked

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

Table of Contents (12) Chapters

Technical requirements

Authors (1)

Personalised recommendations for you

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

Table of Contents (12) Chapters Close

Technical requirements

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you

Table of Contents (12) Chapters