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Machine Learning Engineering  with Python

You're reading from   Machine Learning Engineering with Python Manage the lifecycle of machine learning models using MLOps with practical examples

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781837631964
Length 462 pages
Edition 2nd Edition
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Author (1):
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Andrew P. McMahon Andrew P. McMahon
Author Profile Icon Andrew P. McMahon
Andrew P. McMahon
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Table of Contents (12) Chapters Close

Preface 1. Introduction to ML Engineering 2. The Machine Learning Development Process FREE CHAPTER 3. From Model to Model Factory 4. Packaging Up 5. Deployment Patterns and Tools 6. Scaling Up 7. Deep Learning, Generative AI, and LLMOps 8. Building an Example ML Microservice 9. Building an Extract, Transform, Machine Learning Use Case 10. Other Books You May Enjoy
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...

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