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)
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Technical requirements

The code examples in this chapter will be simpler to follow if you have the following installed and running on your machine:

  • Postman or another API development tool
  • A local Kubernetes cluster manager like minikube or kind
  • The Kubernetes CLI tool, kubectl

There are several different conda environment .yml files contained in the Chapter08 folder in the book’s GitHub repo for the technical examples, as there are a few different sub-components. These are:

  • mlewp-chapter08-train: This specifies the environment for running the training scripts.
  • mlewp-chapter08-serve: This specifies the environment for the local FastAPI web service build.
  • mlewp-chapter08-register: This gives the environment specification for running the MLflow tracking server.

In each case, create the Conda environment, as usual, with:

conda env create –f <ENVIRONMENT_NAME>.yml

The Kubernetes examples in this...