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

Exploring the unreasonable effectiveness of patterns

In this book, we have already mentioned a few times that we should not attempt to reinvent the wheel and we should reuse, repeat, and recycle what works according to the wider software and ML community. This is also true about your deployment architectures. When we discuss architectures that can be reused for a variety of different use cases with similar characteristics, we often refer to these as patterns. Using standard (or at least well-known) patterns can really help you speed up the time to value of your project and help you engineer your ML solution in a way that is robust and extensible.

Given this, we will spend the next few sections summarizing some of the most important architectural patterns that have become increasingly successful in the ML space over the past few years.

Swimming in data lakes

The single most important asset for anyone trying to use ML is, of course, the data that we can analyze and train our models on...