Bücher von Emily Webber

Connecting with other people, finding a sense of belonging and the need for support are natural human desires. Employees who don't feel supported at work don't stay around for long - or if they do, they quickly become unmotivated and unhappy. At a time when organisational structures are flattening and workforces are increasingly fluid, supporting and connecting people is more important than ever. This is where organisational communities of practice come in. Communities of practice have many valuable benefits. They include accelerating professional development; breaking down organisational silos; enabling knowledge sharing and management; building better practice; helping to hire and retain staff; and making people happier. In this book, Emily Webber shares her learning from personal experiences of building successful communities of practice within organisations. And along the way, she gives practical guidance on creating your own.

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 examplesKey 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-tuningPrepare for large-scale training with custom accelerators and GPUsConfigure environments on AWS and SageMaker to maximize performanceSelect hyperparameters based on your model and constraintsDistribute your model and dataset using many types of parallelismAvoid pitfalls with job restarts, intermittent health checks, and moreEvaluate your model with quantitative and qualitative insightsDeploy your models with runtime improvements and monitoring pipelinesWho this book is 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.