Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter

Applied Machine Learning and High-Performance Computing on AWS (eBook, ePUB)

Accelerate the development of machine learning applications following architectural best practices

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 382
• Erscheinungstermin: 30. Dezember 2022
• Englisch
• ISBN-13: 9781803244440
• Artikelnr.: 70225146

Autorenporträt

Mani Khanuja is a seasoned IT professional with over 17 years of software engineering experience. She has successfully led machine learning and artificial intelligence projects in various domains, such as forecasting, computer vision, and natural language processing. At AWS, she helps customers to build, train, and deploy large machine learning models at scale. She also specializes in data preparation, distributed model training, performance optimization, machine learning at the edge, and automating the complete machine learning life cycle to build repeatable and scalable applications.