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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Selecting the tools

For this example, and pretty much whenever we have an ETML problem, our main considerations boil down to a few simple things, namely the selection of the interfaces we need to build, the tools we need to perform the transformation and modeling at the scale we require, and how we orchestrate all of the pieces together. The next few sections will cover each of these in turn.

Interfaces and storage

When we execute the extract and load parts of ETML, we need to consider how to interface with the systems that store our data. It is important that whichever database or data technology we extract from, we use the appropriate tools to extract at whatever scale and pace we need. In this example, we can use S3 on AWS for our storage; our interfacing can be taken care of by the AWS boto3 library and the AWS CLI. Note that we could have selected a few other approaches, some of which are listed in Table 9.2 along with their pros and cons.