PRETRAIN VISION AND LARGE LANGUAGE MODELS IN PYTHON

Master the art of training vision and large language models with conceptual fundaments and industry-expert guidance. Learn about AWS services and design patterns, with relevant coding examples

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.

Book Description:
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.

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