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)
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In this chapter, we will dive into some important concepts around the deployment of your Machine Learning (ML) solution. We will begin to close the circle of the ML development life cycle and lay the groundwork for getting your solutions out into the world.

The act of deploying software, of taking it from a demo you can show off to a few stakeholders to a service that will ultimately impact customers or colleagues, is a very exhilarating but often challenging exercise. It also remains one of the most difficult aspects of any ML project and getting it right can ultimately make the difference between generating value or just hype.

We are going to explore some of the main concepts that will help your ML engineering team cross the chasm between a fun proof-of-concept to solutions that can run on scalable infrastructure in an automated way.

In this chapter, we will cover the following topics:

  • Architecting systems...