Artificial Intelligence And High Performance Computing In The Cloud
Download Artificial Intelligence And High Performance Computing In The Cloud PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Artificial Intelligence And High Performance Computing In The Cloud book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Artificial Intelligence and High Performance Computing in the Cloud
High-performance computing as significantly evolves during the last two decades with the advent and consideration of multicores configurations and general-purpose GPUs. In the same time but more recently, artificial intelligence has gained a serious popularity and is considered in a broad range of applications. Cutting-edge AI, which typically involving large models and huge volume of training datasets, cannot be considered without the support of HPC infrastructures and techniques. Cloud computing is the most affordable way to leverage the power of HPC systems. As collection of valuable inputs from high-standard scientists, “Artificial Intelligence and High-Performance Computing in the Cloud: Upcoming” is certainly a book that should be given a serious consideration.
High-performance Big Data Computing
"This book explores how to achieve high performance and scalability for big data middleware and applications"--
Applied Machine Learning and High-Performance Computing on AWS
Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMaker Key FeaturesUnderstand the need for high-performance computing (HPC)Build, train, and deploy large ML models with billions of parameters using Amazon SageMakerLearn best practices and architectures for implementing ML at scale using HPCBook Description Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle. What you will learnExplore data management, storage, and fast networking for HPC applicationsFocus on the analysis and visualization of a large volume of data using SparkTrain visual transformer models using SageMaker distributed trainingDeploy and manage ML models at scale on the cloud and at the edgeGet to grips with performance optimization of ML models for low latency workloadsApply HPC to industry domains such as CFD, genomics, AV, and optimizationWho this book is for The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.