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

Summary

This chapter has covered how to apply a lot of the techniques learned in this book, in particular from Chapter 2, The Machine Learning Development Process, Chapter 3, From Model to Model Factory, Chapter 4, Packaging Up, and Chapter 5, Deployment Patterns and Tools, to a realistic application scenario. The problem, in this case, concerned clustering taxi rides to find anomalous rides and then performing NLP on some contextual text data to try and help explain those anomalies automatically. This problem was tackled using the ETML pattern, which I offered up as a way to rationalize typical batch ML engineering solutions. This was explained in detail. A design for a potential solution, as well as a discussion of some of the tooling choices any ML engineering team would have to go through, was covered. Finally, a deep dive into some of the key pieces of work that would be required to make this solution production-ready was performed. In particular we showed how you can use good...