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

Containerizing

If you develop software that you want to deploy somewhere, which is the core aim of an ML engineer, then you have to be very aware of the environmental requirements of your code, and how different environments might affect the ability of your solution to run. This is particularly important for Python, which does not have a core capability for exporting programs as standalone executables (although there are options for doing this). This means that Python code needs a Python interpreter to run and needs to exist in a general Python environment where the relevant libraries and supporting packages have been installed.

A great way to avoid headaches from this point of view is to ask the question: Why can't I just put everything I need into something that is relatively isolated from the host environment, which I can ship and then run as a standalone application or program? The answer to this question is that you can and that you do this through containerization. This is...