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
Other Books You May Enjoy
11
Index

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In previous chapters, we introduced a lot of the tools and techniques you will need to use to successfully build working Machine Learning (ML) products. We also introduced a lot of example pieces of code that helped us to understand how to implement these tools and techniques. So far, this has all been about what we need to program, but this chapter will focus on how to program. In particular, we will introduce and work with a lot of the techniques, methodologies, and standards that are prevalent in the wider Python software development community and apply them to ML use cases. The conversation will be centered around the concept of developing user-defined libraries and packages, reusable pieces of code that you can use for deploying your ML solutions or for developing new ones. It is important to note that everything we discuss here can be applied to all of your Python development activities across your ML...