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 was all about building a solid foundation for future work. We discussed the development steps common to all ML engineering projects, which we called discover, play, develop, deploy. In particular, we outlined the aim of each of these steps and their desired outputs.

This was followed by a high-level discussion of tooling, and a walkthrough of the main setup steps. We set up the tools for developing our code, keeping track of the changes of that code, managing our ML engineering project, and finally, deploying our solutions.

In the rest of the chapter, we went through the details for each of the four stages we outlined previously, with a particular focus on the develop and deploy stages. Our discussion covered everything from the pros and cons of Waterfall and Agile development methodologies to environment management and then software development best practices. We also discussed how to apply testing to our ML code. We finished off with an exploration of how to package...