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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

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804618257
Length 258 pages
Edition 1st Edition
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Author (1):
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Emily Webber Emily Webber
Author Profile Icon Emily Webber
Emily Webber
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Table of Contents (23) Chapters Close

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 22. Other Books You May Enjoy

What is model deployment?

After you’ve spent weeks to months working on your custom model, from optimizing the datasets to the distributed training environment, evaluating it, and reducing bias, you must be hungry to finally release it to your customers! In this entire section of the book, we’ll focus on all the key topics related to model deployment. But first, let’s try to explain the term itself.

Model deployment refers to integrating your model into an application. It means that beyond using your model for local analysis in a notebook, or for running reports, you connect it to other software applications. Most commonly, you’re integrating that model into an application. This application could be simply an analytics dashboard. It might be a fraud detection system, a natural language chat, a general search, an autonomous vehicle, or even a video game. In the next chapter, we’ll provide even more ideas for use cases across organizations, especially...

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