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Generative AI with Amazon Bedrock

You're reading from   Generative AI with Amazon Bedrock Build, scale, and secure generative AI applications using Amazon Bedrock

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781803247281
Length 384 pages
Edition 1st Edition
Tools
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Authors (2):
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Shikhar Kwatra Shikhar Kwatra
Author Profile Icon Shikhar Kwatra
Shikhar Kwatra
Bunny Kaushik Bunny Kaushik
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Bunny Kaushik
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Table of Contents (18) Chapters Close

Preface 1. Part 1: Amazon Bedrock Foundations FREE CHAPTER
2. Chapter 1: Exploring Amazon Bedrock 3. Chapter 2: Accessing and Utilizing Models in Amazon Bedrock 4. Chapter 3: Engineering Prompts for Effective Model Usage 5. Chapter 4: Customizing Models for Enhanced Performance 6. Chapter 5: Harnessing the Power of RAG 7. Part 2: Amazon Bedrock Architecture Patterns
8. Chapter 6: Generating and Summarizing Text with Amazon Bedrock 9. Chapter 7: Building Question Answering Systems and Conversational Interfaces 10. Chapter 8: Extracting Entities and Generating Code with Amazon Bedrock 11. Chapter 9: Generating and Transforming Images Using Amazon Bedrock 12. Chapter 10: Developing Intelligent Agents with Amazon Bedrock 13. Part 3: Model Management and Security Considerations
14. Chapter 11: Evaluating and Monitoring Models with Amazon Bedrock 15. Chapter 12: Ensuring Security and Privacy in Amazon Bedrock 16. Index 17. Other Books You May Enjoy

Analyzing the results

As mentioned previously, when creating a customization job, we provide an output S3 path, where the metrics and logs are stored by the training job. You will see the step_wise_training_metrics.csv and validation_metrics.csv files inside the S3 output path. Within these files, you will see information such as the step number, epoch number, loss, and perplexity. You will see these details in both the training and validation sets. Although providing a validation set is optional, doing so allows the performance metrics of the custom model that’s been created to be evaluated.

Depending on the size of the dataset, you can decide how much of the validation dataset you would like to hold. If your dataset is small (for example, it contains hundreds or thousands of records), you can use 90% as the training set and 10% as the validation set. If your dataset size is large (for example, it contains hundreds of thousands of records), you can reduce the validation...

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