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 introduced the idea of ML engineering and how that fits within a modern team building valuable solutions based on data. There was a discussion of how the focus of ML engineering is complementary to the strengths of data science and data engineering and where these disciplines overlap. Some comments were made about how to use this information to assemble an appropriately resourced team for your projects.

The challenges of building ML products in modern real-world organizations were then discussed, along with pointers to help you overcome some of these challenges. In particular, the notions of reasonably estimating value and effectively communicating with your stakeholders were emphasized.

This chapter then rounded off with a taster of the technical content to come in later chapters, through a discussion of what typical ML solutions look like and how they should be designed (at a high level) for some common use cases.

These topics are important...