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

Architecting systems

No matter how you are working to build your software, it is always important to have a design in mind. This section will highlight the key considerations we must bear in mind when architecting ML systems.

Consider a scenario where you are contracted to organize the building of a house. We would not simply go out and hire a team of builders, buy all the supplies, hire all the equipment, and just tell everyone to start building. We would also not assume we knew exactly what the client who hired us wants without first speaking to them.

Instead, we would likely try to understand what the client wanted in detail, and then try to design the solution that would fit their requirements. We would potentially iterate this plan a few times with them and with appropriate experts who knew the details of pieces that fed into the overall design. Although we are not interested in building houses (or maybe you are, but there will not be any in this book!), we can still see the analogy...