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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Testing, logging, securing and error handling

Building code that performs an ML task may seem like the end goal, but it is only one piece of the puzzle. We also want to be confident that this code will work and if it doesn't, we will be able to fix it. This is where the concepts of testing, logging, and error handling come in, which the next few sections cover at a high level.


One of the most important features that sets your ML engineered code apart from typical research scripts is the presence of robust testing. It is critical that any system you are designing for deployment can be trusted not to fall down all the time and that you can catch issues during the development process.

Luckily, since Python is a general-purpose programming language, it is replete with tools for performing tests on your software. In this chapter, we will use PyTest, which is one of the most popular, powerful, and easy-to-use testing toolsets for Python code available. PyTest is particularly useful...