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

In this chapter, we walked through an example of how to take the tools and techniques from the first seven chapters of this book and apply them together to solve a realistic business problem. We discussed in detail how the need for a dynamically triggered forecasting algorithm can lead very quickly to a design that requires several small services to interact seamlessly. In particular, we created a design with components responsible for handling events, training models, storing models, and performing predictions. We then walked through how we would choose our toolset to build to this design in a real-world scenario, by considering things such as appropriateness for the task at hand, as well as likely developer familiarity. Finally, we carefully defined the key pieces of code that would be required to build the solution to solve the problem repeatedly and robustly.

In the next, and final, chapter, we will build out an example of a batch ML process. We will name the pattern...