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Key benefits

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

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.

Who is this book for?

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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Building AI Applications with ChatGPT APIs

$44.99

Modern Generative AI with ChatGPT and OpenAI Models

$49.99

Pretrain Vision and Large Language Models in Python

$49.99

Total $ 144.97

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alla Jul 07, 2023

This compelling guide is a treasure for machine learning engineers aspiring to dive deep into working within the AWS ecosystem.It proficiently lays out the practices of model algorithms and development, from selecting the ideal design and training dataset to deploying on AWS.With the author's expertise and insightful predictions, readers are navigated through the wonders of Sagemaker, model evaluation, and future trends.Highly recommended for those ready to embark on this awesome journey.

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Pratik Bhavsar Oct 03, 2023

If you've ever wondered about the intricacies of building massive language models, this book is an absolute must-read. It's jam-packed with invaluable insights on how to harness the full power of AWS to supercharge your language model workflows. 💡While there's no shortage of high-level content on the subject, what sets this book apart is its deep dive into the nitty-gritty, low-level details that are often elusive but oh-so-critical in this field.

Matthew Nightingale Jul 19, 2023

I would recommend this book to anyone (technical and non-technical) who is interested in learning about training foundational models. Given the explosion of GenAI and more specifically foundational models in 2023, I purchased this book to learn more about when, why, and how to pre-train these models for specific use-cases. As someone who is new to machine learning, let alone pre-training foundational models, I have found this book very digestable. It does a great job bridging high level considerations with deep and thoughtful technical guidance, and provides opinionated guidance on each step of the decesion making process.

Sangita Mahala Jul 31, 2023

This book is a fantastic resource for anyone interested in learning how to train and use vision and language models in Python. The book is thorough, well-written, and simple to read. It will, in my opinion, be a useful resource for anyone with an interest in this subject.

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

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