Book Image

Machine Learning Engineering with Python - Second Edition

By : Andrew P. McMahon
2.5 (2)
Book Image

Machine Learning Engineering with Python - Second Edition

2.5 (2)
By: Andrew P. McMahon

Overview of this book

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.
Table of Contents (12 chapters)
10
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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
  • ...