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

Packaging your code

In some ways, it is interesting that Python has taken the world by storm. It is dynamically typed and non-compiled, so it can be quite different to work with compared to Java or C++. This particularly comes to the fore when we think about packaging our Python solutions. For a compiled language, the main target is to produce a compiled artifact that can run on the chosen environment, a Java jar for example. Python requires that the environment you run in has an appropriate Python interpreter and the ability to install the libraries and packages you need. There is also no single compiled artifact created, so you often need to deploy your whole code base as is.

Despite this, Python has indeed taken the world by storm, especially for ML. As we are ML engineers thinking about taking models to production, we would be remiss to not understand how to package and share Python code in a way that helps others to avoid repetition, to trust in the solution, and to be able to easily...