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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

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Modern Generative AI with ChatGPT and OpenAI Models

€37.99

Pretrain Vision and Large Language Models in Python

€37.99

Total € 109.97

N/A Jan 19, 2024

Feefo Verified review

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.

Amazon Verified review

Steven Fernandes Aug 11, 2023

In this comprehensive guide, readers are taken on a journey through the intricacies of machine learning model optimization, starting with the selection of ideal use cases and datasets for pretraining and fine-tuning. The book stands out in its detailed discussions on harnessing the power of custom accelerators and GPUs for large-scale training. It provides a hands-on tutorial on configuring AWS and SageMaker environments for optimal performance, which many practitioners will find invaluable. One of the key highlights is the chapter on hyperparameter selection, where the relationship between model constraints and optimal settings is delved into with clarity. The sections on model and dataset distribution using various parallelism techniques offer insights into efficient data handling. Practical advice, such as how to navigate challenges like job restarts and health checks, ensures readers are well-equipped to handle real-world scenarios. Finally, the guide culminates with thorough methods to evaluate and deploy models, emphasizing both quantitative and qualitative insights, and the importance of effective runtime improvements and monitoring. A must-read for anyone keen on mastering the art of machine learning at scale.

Amazon Verified review

Deep P. Jul 31, 2023

Pretrain Vision and Large Language Models in Python is a book by Emily Webber that teaches you how to train and deploy foundation models on AWS. The book covers a wide range of topics, from the basics of pretraining to the deployment of models on SageMaker.The book is well-written and easy to follow, and it includes a number of helpful resources, such as code samples, tutorials, and links to additional information. The author does a great job of explaining complex concepts in a clear and concise way, and she provides plenty of examples to help readers understand how to train and deploy foundation models on AWS.One of the things I really liked about this book is that it goes beyond just explaining the concepts. The author also provides guidance on how to use these concepts to solve real-world problems. For example, she shows how to use foundation models to build applications that can automatically generate text, translate languages, and answer questions.Overall, I thought Pretrain Vision and Large Language Models in Python was an excellent resource for anyone who wants to learn more about foundation models or who wants to use these models to build their own applications. The book is well-written, informative, and easy to follow. I highly recommend it.

Amazon Verified review

Om S Jun 05, 2023

As a long-time admirer of Emily Webber and an enthusiast of SageMaker and Large Language Models (LLMs), I've been eagerly anticipating the release of this book. As a regular user of AWS, I can confidently state that this book is a substantial contribution to the advanced field of Machine Learning (ML).In the fast-paced world of ML, where LLMs are at the forefront of innovation, keeping up with the rapid advancements can be challenging. However, Emily's book, announced just before the LLM boom, provides a solid foundation that enables you to navigate these developments with ease. It has proven to be a timely resource for those keen on understanding and leveraging the power of LLMs.Book Summary:Comprehensive Coverage: The book offers an in-depth exploration of training vision and large language models, covering all stages from project ideation, dataset preparation, training, evaluation, to deployment for large language, vision, and multimodal models.Expert Guidance: Authored by Emily Webber, a seasoned AWS and machine learning expert, the book provides industry-expert guidance and practical advice, making it a valuable resource for both beginners and experienced practitioners.Practical Approach: The book is replete with practical examples and code samples that help readers understand how to pretrain and fine-tune their own foundation models on AWS and Amazon SageMaker.Bias Detection: A unique feature of the book is its focus on bias detection and pipeline monitoring, which are critical aspects of model development and deployment.Advanced Topics: The book delves into advanced topics like large-scale distributed training, hyperparameter selection, and model distribution, providing readers with a deep understanding of these complex areas.Future Trends: The final chapter on future trends in pretraining foundation models gives readers a glimpse into what's next in the field, keeping them ahead of the curve.In conclusion, if you're looking to ride the wave of LLMs and want to do so using AWS, this book is a must-read. It's more than just a guide; not a beginner's book!!! It's a comprehensive resource that empowers you to navigate the fast-paced world of ML with confidence and proficiency. Emily Webber's expertise shines through each page, making this book an invaluable asset for anyone in the field. As LLMs continue to evolve and revolutionize various sectors, this book stands as a testament to their transformative potential and a guide for those looking to be part of this exciting journey. This is just the start..... Transformers......... !!!

Amazon Verified review

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

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