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

Hosting your own microservice on AWS

A classic way to surface your ML models is via a lightweight web service hosted on a server. This can be a very flexible pattern of deployment. You can run a web service on any server with access to the internet (roughly) and, if designed well, it is often easy to add further functionality to your web service and expose it via new endpoints.

In Python, the two most used web frameworks have always been Django and Flask. In this section, we will focus on Flask as it is the simpler of the two and has been written about extensively for ML deployments on the web, so you will be able to find plenty of material to build on what you learn here.

On AWS, one of the simplest ways you can host your Flask web solution is as a containerized application on an appropriate platform. We will go through the basics of doing this here, but we will not spend time on the detailed aspects of maintaining good web security for your service. To fully discuss this may require...