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 batch processing problem

In Chapter 1, Introduction to ML Engineering, we saw the scenario of a taxi firm that wanted to analyze anomalous rides at the end of every day. The customer had the following requirements:

  • Rides should be clustered based on ride distance and time, and anomalies/outliers identified.
  • Speed (distance/time) was not to be used, as analysts would like to understand long-distance rides or those with a long duration.
  • The analysis should be carried out on a daily schedule.
  • The data for inference should be consumed from the company’s data lake.
  • The results should be made available for consumption by other company systems.

Based on the description in the introduction to this chapter, we can now add some extra requirements:

  • The system’s results should contain information on the rides classification as well as a summary of relevant textual data.
  • Only anomalous rides need to have...