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

Deep Learning, Generative AI, and LLMOps

The world is changing. Fast. At the time of writing in mid-2023, machine learning (ML) and artificial intelligence (AI) have entered the public consciousness in a way that even a few months ago seemed impossible. With the rollout of ChatGPT in late 2022, as well as a wave of new tools from labs and organizations across the world, hundreds of millions of people are now using ML solutions every day to create, analyze, and develop. On top of this, innovation seems to only be speeding up, with what seems like a new announcement of a record-beating model or new tool every day. ChatGPT is only one example of a solution that uses what is now known as generative artificial intelligence (generative AI or GenAI). While ChatGPT, Bing AI, and Google Bard are examples of text-based generative AI tools, there is also DALL-E and Midjourney in the image space and now a whole suite of multi-modal models combining these and other types of data. Given the complexity...