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

Serving the models with FastAPI

The simplest and potentially most flexible approach to serving ML models in a microservice with Python is in wrapping the serving logic inside a lightweight web application. Flask has been a popular option among Python users for many years but now the FastAPI web framework has many advantages, which means it should be seriously considered as a better alternative.

Some of the features of FastAPI that make it an excellent choice for a lightweight microservice are:

  • Data validation: FastAPI uses and is based on the Pydantic library, which allows you to enforce type hints at runtime. This allows for the implementation of very easy-to-create data validation steps that make your system way more robust and helps avoid edge case behaviors.
  • Built-in async workflows: FastAPI gives you asynchronous task management out of the box with async and await keywords, so you can build the logic you will need in many cases relatively seamlessly without...