{"product_id":"applied-machine-learning-and-high-performance-computing-on-aws-accelerate-the-development-of-machine-learning-applications-following-architectural-be-paperback","title":"Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural be - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eMani Khanuja\u003c\/b\u003e (Author), \u003cb\u003eFarooq Sabir\u003c\/b\u003e (Author), \u003cb\u003eShreyas Subramanian\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBuild, 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\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand the need for high-performance computing (HPC)\u003c\/li\u003e\n\u003cli\u003eBuild, train, and deploy large ML models with billions of parameters using Amazon SageMaker\u003c\/li\u003e\n\u003cli\u003eLearn best practices and architectures for implementing ML at scale using HPC\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eMachine 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.\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eBy 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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eExplore data management, storage, and fast networking for HPC applications\u003c\/li\u003e\n\u003cli\u003eFocus on the analysis and visualization of a large volume of data using Spark\u003c\/li\u003e\n\u003cli\u003eTrain visual transformer models using SageMaker distributed training\u003c\/li\u003e\n\u003cli\u003eDeploy and manage ML models at scale on the cloud and at the edge\u003c\/li\u003e\n\u003cli\u003eGet to grips with performance optimization of ML models for low latency workloads\u003c\/li\u003e\n\u003cli\u003eApply HPC to industry domains such as CFD, genomics, AV, and optimization\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 382\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.79 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 30, 2022\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47214261043449,"sku":"9781803237015","price":70.54,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/JgjQ__0Qg-9781803237015.webp?v=1768112478","url":"https:\/\/bookscloud.io\/products\/applied-machine-learning-and-high-performance-computing-on-aws-accelerate-the-development-of-machine-learning-applications-following-architectural-be-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}