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

Building the model factory with pipelines

The concept of a software pipeline is intuitive enough. If you have a series of steps chained together in your code, so that the next step consumes or uses the output of the previous step or steps, then you have a pipeline.

In this section, when we refer to a pipeline, we will be specifically dealing with steps that contain processing or calculations that are appropriate to ML. For example, the following diagram shows how this concept may apply to some of the steps the marketing classifier mentioned in Chapter 1, Introduction to ML Engineering:

Figure 3.11 – The main stages of any training pipeline and how this maps to a specific case from Chapter 1, Introduction to ML Engineering.

Let's discuss some of the standard tools for building up your ML pipelines in code.

Scikit-learn pipelines

Our old friend scikit-learn comes packaged with some nice pipelining functionality. At the time of writing, scikit-learn versions greater than...