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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

AWS offerings for MLOps

Happily, AWS provides a variety of tools to help simplify this! One nice feature is called lineage tracking. SageMaker can automatically create the lineage (3) for key artifacts, including across accounts. This includes dataset artifacts, images, algorithm specifications, data configs, training job components, endpoints, and checkpoints. This is integrated with the Experiments SDK, letting you compare experiments and results programmatically and at scale. Let’s explore this visually. We’ll even generate a visualization for you to see how all of these are connected! Check it out in the following figure.

Figure 14.5 – SageMaker automatically creates lineage tracking

Figure 14.5 – SageMaker automatically creates lineage tracking

As you can see, the first step in tracking your lineage is running on key SageMaker resources such as training jobs, images, and processing jobs. You can use the entities that are automatically tracked, or you can define your own entities. To generate...

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