Foundation models have forever changed machine learning. From BERT to ChatGPT, CLIP to Stable Diffusion, when billions of parameters are combined with large datasets and hundreds to thousands of GPUs, the result is nothing short of record-breaking. The recommendations, advice, and code samples in this book will help you pretrain and fine-tune your own foundation models from scratch on AWS and Amazon SageMaker, while applying them to hundreds of use cases across your organization.

With advice from seasoned AWS and machine learning expert Emily Webber, this book helps you learn everything you need to go from project ideation to dataset preparation, training, evaluation, and deployment for large language, vision, and multimodal models. With step-by-step explanations of essential concepts and practical examples, you’ll go from mastering the concept of pretraining to preparing your dataset and model, configuring your environment, training, fine-tuning, evaluating, deploying, and optimizing your foundation models.

You will learn how to apply the scaling laws to distributing your model and dataset over multiple GPUs, remove bias, achieve high throughput, and build deployment pipelines.

By the end of this book, you’ll be well equipped to embark on your own project to pretrain and fine-tune the foundation models of the future.

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Spis treści

Pretrain Vision and Large Language Models in Python. End-to-end techniques for building and deploying foundation models on AWS eBook -- spis treści

• 0. Product Information Document
• 1. An Introduction to Pretraining Foundation Models
• 2. Dataset Preparation: Part One
• 3. Model Preparation
• 4. Containers and Accelerators on the Cloud
• 5. Distribution Fundamentals
• 6. Dataset Preparation: Part Two, the Data Loader
• 7. Finding the Right Hyperparameters
• 8. Large-Scale Training on SageMaker
• 9. Advanced Training Concepts
• 10. Fine-Tuning and Evaluating
• 11. Detecting, Mitigating, and Monitoring Bias
• 12. How to Deploy Your Model
• 13. Prompt Engineering
• 14. MLOps for Vision and Language
• 15. Future Trends in Pretraining Foundation Models