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

Building the future with LLMOps

Given the rise in interest in LLMs recently, there has been no shortage of people expressing the desire to integrate these models into all sorts of software systems. For us as ML engineers, this should immediately trigger us to ask the question, “What will that mean operationally?” As discussed throughout this book, the marrying together of operations and development of ML systems is termed MLOps. Working with LLMs is likely to lead to its own interesting challenges, however, and so a new term, LLMOps, has arisen to give this sub-field of MLOps some good marketing.

Is this really any different? I don’t think it is that different, but should be viewed as a sub-field of MLOps with its own additional challenges. Some of the main challenges that I see in this area are:

  • Larger infrastructure, even for fine-tuning: As discussed previously, these models are far too large for typical organizations or teams to consider training...