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

Understanding the forecasting problem

In Chapter 1, Introduction to ML Engineering, we considered the example of an ML team that has been tasked with providing forecasts of items at the level of individual stores in a retail business. The fictional business users had the following requirements:

  • The forecasts should be rendered and accessible via a web-based dashboard.
  • The user should be able to request updated forecasts if necessary.
  • The forecasts should be carried out at the level of individual stores.
  • Users will be interested in their own regions/stores in any one session and not be concerned with global trends.
  • The number of requests for updated forecasts in any one session will be small.

Given these requirements, we can work with the business to create the following user stories, which we can put into a tool such as Jira, as explained in Chapter 2, The Machine Learning Development Process. Some examples of user stories covering these...