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

Setting up an AWS account

As previously stated, you don't have to use AWS, but that's what we're going to use throughout this book. Once it's set up here, you can use it for everything we'll do:

  1. To set up an AWS account, navigate to aws.amazon.com and select Create Account. You will have to add some payment details but everything we mention in this book can be explored through the free tier of AWS, where you do not incur a cost below some set threshold of consumption.
  2. Once you have created your account, you can navigate to the AWS Management Console, where you can see all of the services that are available to you (see Figure 2.5):

    Figure 2.5 – The AWS Management Console
  3. Finally, there would be no ML engineering without ML models. So, the final piece of software you should install is one that will help you track and serve your models in a consistent way. For this, we will use MLflow, an open source platform from Databricks and under the stewardship of...