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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

Living it large with LLMs

At the time of writing, GPT-4 has been released only a few months previously, in March 2023, by OpenAI. This model is potentially the largest ML model ever developed, with a reported one trillion parameters, although OpenAI has not confirmed the exact number. Since then, Microsoft and Google have announced advanced chat capabilities using similarly large models in their product suites and a raft of open-source packages and toolkits have been released. All of these solutions leverage some of the largest neural network models ever developed, LLMs. LLMs are part of an even wider class of models known as foundation models, which span not just text applications but video and audio as well. These models are roughly classified by the author as being too large for most organizations to consider training from scratch. This will mean organizations will either consume these models as third-party services or host and then fine-tune existing models. Solving this integration...

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