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Pretrain Vision and Large Language Models in Python

You're reading from Pretrain Vision and Large Language Models in Python End-to-end techniques for building and deploying foundation models on AWS

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781804618257

Length 258 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

GPT/LLMs

Author (1):

Emily Webber

View More author details

Table of Contents (23) Chapters

Preface

1. Part 1: Before Pretraining

2. Chapter 1: An Introduction to Pretraining Foundation Models FREE CHAPTER

3. Chapter 2: Dataset Preparation: Part One

4. Chapter 3: Model Preparation

5. Part 2: Configure Your Environment

6. Chapter 4: Containers and Accelerators on the Cloud

7. Chapter 5: Distribution Fundamentals

8. Chapter 6: Dataset Preparation: Part Two, the Data Loader

9. Part 3: Train Your Model

10. Chapter 7: Finding the Right Hyperparameters

11. Chapter 8: Large-Scale Training on SageMaker

12. Chapter 9: Advanced Training Concepts

13. Part 4: Evaluate Your Model

14. Chapter 10: Fine-Tuning and Evaluating

15. Chapter 11: Detecting, Mitigating, and Monitoring Bias

16. Chapter 12: How to Deploy Your Model

17. Part 5: Deploy Your Model

18. Chapter 13: Prompt Engineering

19. Chapter 14: MLOps for Vision and Language

20. Chapter 15: Future Trends in Pretraining Foundation Models

21. Index

Why subscribe?

22. Other Books You May Enjoy

Part 5: Deploy Your Model

In part 5, you’ll learn how to deploy your model. You’ll use techniques such as distillation, quantization, and compilation to reduce your model’s overall footprint. You’ll identify top use cases to scale your model across organizations, and learn about ongoing operations, monitoring, and maintenance.

This section has the following chapters:

Chapter 13, Prompt Engineering
Chapter 14, MLOps for Vision and Language
Chapter 15, Future Trends in Pretraining Foundation Models

The rest of the chapter is locked

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Authors (1)

Emily Webber

Emily Webber

Emily Webber is a Principal Machine Learning Specialist Solutions Architect at Amazon Web Services. She has assisted hundreds of customers on their journey to ML in the cloud, specializing in distributed training for large language and vision models. She mentors Machine Learning Solution Architects, authors countless feature designs for SageMaker and AWS, and guides the Amazon SageMaker product and engineering teams on best practices in regards around machine learning and customers. Emily is widely known in the AWS community for a 16-video YouTube series featuring SageMaker with 160,000 views, plus a Keynote at O'Reilly AI London 2019 on a novel reinforcement learning approach she developed for public policy.

See other products by Emily Webber

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