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

Summary

In this chapter, we focused on deep learning. In particular, we covered the key theoretical concepts behind deep learning, before moving on to discuss how to build and train your own neural networks. We walked through examples of using off-the-shelf models for inference and then adapting them to your specific use cases through fine-tuning and transfer learning. All of the examples shown were based on heavy use of the PyTorch deep learning framework and the Hugging Face APIs.

We then moved on to the topical question of the largest models ever built, LLMs, and what they mean for ML engineering. We explored a little of their important design principles and behaviors before showing how to interact with them in pipelines using the popular LangChain package and OpenAI APIs. We also explored the potential for using LLMs to help with improving software development productivity, and what this will mean for you as an ML engineer.

We finished the chapter with an exploration of...