Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Machine Learning Engineering  with Python

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

Arrow left icon
Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781837631964
Length 462 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Andrew P. McMahon Andrew P. McMahon
Author Profile Icon Andrew P. McMahon
Andrew P. McMahon
Arrow right icon
View More author details
Toc

Table of Contents (12) Chapters Close

Preface 1. Introduction to ML Engineering 2. The Machine Learning Development Process FREE CHAPTER 3. From Model to Model Factory 4. Packaging Up 5. Deployment Patterns and Tools 6. Scaling Up 7. Deep Learning, Generative AI, and LLMOps 8. Building an Example ML Microservice 9. Building an Extract, Transform, Machine Learning Use Case 10. Other Books You May Enjoy
11. Index

Building an Example ML Microservice

This chapter will be all about bringing together some of what we have learned in the book so far with a realistic example. This will be based on one of the scenarios introduced in Chapter 1, Introduction to ML Engineering, where we were required to build a forecasting service for store item sales. We will discuss the scenario in a bit of detail and outline the key decisions that have to be made to make a solution a reality, before showing how we can employ the processes, tools, and techniques we have learned through out this book to solve key parts of the problem from an ML engineering perspective. By the end of this chapter, you should come away with a clear view of how to build your own ML microservices for solving a variety of business problems.

In this chapter, we will cover the following topics:

  • Understanding the forecasting problem
  • Designing our forecasting service
  • Selecting the tools
  • Training at scale
  • ...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at AU $24.99/month. Cancel anytime