Applied Machine Learning and High-Performance Computing on AWS

Dive into the fascinating world of applied machine learning with high-performance computing on AWS. This book combines the fundamentals of HPC with practical techniques to harness the power of cloud-based machine learning. By exploring end-to-end workflows, you will learn best practices to design, train, and deploy scalable ML applications.

What this Book will help me do

• Explore foundational concepts of high-performance computing in combination with machine learning applications.
• Understand data storage, networking, and compute strategies for HPC workloads.
• Learn how to train and deploy massive ML models using distributed computing on AWS.
• Master performance tuning and optimization techniques for ML models in production environments.
• Develop practical knowledge for applying HPC-driven ML solutions in industries such as genomics and autonomous vehicles.

Author(s)

Mani Khanuja, Farooq Sabir, Shreyas Subramanian, and Trenton Potgieter bring a wealth of expertise in machine learning and cloud computing. Together, they have extensive experience in implementing scalable ML applications in various domains. Their unique insights provide practical guidance for learners aiming to excel in ML with AWS.

Who is it for?

This book is designed for machine learning engineers and data scientists with prior knowledge of ML principles, aiming to expand into high-performance computing. It is also suitable for domain specialists in genomics, autonomous vehicles, or CFD who wish to integrate ML principles using AWS. With its practical examples, it's an asset for professionals aiming to enhance their skillset in scalable cloud solutions.