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.
publication date
2023-05-31
what you will learn
Find the right use cases and datasets for pretraining and fine-tuning
Prepare for large-scale training with custom accelerators and GPUs
Configure environments on AWS and SageMaker to maximize performance
Select hyperparameters based on your model and constraints
Distribute your model and dataset using many types of parallelism
Avoid pitfalls with job restarts, intermittent health checks, and more
Evaluate your model with quantitative and qualitative insights
Deploy your models with runtime improvements and monitoring pipelines
key features
Learn to develop, train, tune, and apply foundation models with optimized end-to-end pipelines * Explore large-scale distributed training for models and datasets with AWS and SageMaker examples * Evaluate, deploy, and operationalize your custom models with bias detection and pipeline monitoring
approach
Readers start with pretraining fundamentals, taking inspiration from vision, text, and multimodal work. Next, we’ll dive into distribution concepts and AWS configuration best practices. You’ll learn how to pick the right hyperparameters and optimize the training loop, learning from case studies like OPT, BLOOM, and more. Finally, you’ll evaluate and deploy your model with an end-to-end pipeline.
audience
If you’re a machine learning researcher or enthusiast who wants to start a foundation modelling project, this book is for you. Applied scientists, data scientists, machine learning engineers, solution architects, product managers, and students will all benefit from this book. Intermediate Python is a must, along with introductory concepts of cloud computing. A strong understanding of deep learning fundamentals is needed, while advanced topics will be explained. The content covers advanced machine learning and cloud techniques, explaining them in an actionable, easy-to-understand way.
short description
Foundation models are the future of machine learning and the backbone of Generative AI. Pretrain Vision and Large Language Models in Python provides you with the conceptual fundamentals, industry-proven best practices, and code snippets you need to help you get started on your large modeling projects at work, in research labs, or just for fun.
subtitle
End-to-end techniques for building and deploying foundation models on AWS
keywords
Machine learning python, Gpt-4, Gpt-4 book, Dalle–e 2, Exploring gpt-3, gpt-3 book
Product ISBN
9781804618257
