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

AutoML

The final level of our hierarchy is the one where we, as the engineer, have the least direct control over the training process, but where we also potentially get a good answer for very little effort!

The development time that's required to search through many hyperparameters and algorithms for your problem can be large, even when you code up reasonable-looking search parameters and loops.

Given this, the past few years have seen the deployment of several AutoML libraries and tools in a variety of languages and software ecosystems. The hype surrounding these techniques has meant they have had a lot of airtime, which has led to several data scientists questioning when their jobs will be automated away. As we mentioned previously in this chapter, in my opinion, declaring the death of data science is extremely premature and also dangerous from an organizational and business performance standpoint. These tools have been given such a pseudo-mythical status that many companies could...