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.
Understanding the need for High Performance Computing (HPC).
Build, train, and deploy large ML models with billions of parameters using Amazon SageMaker.
Best practices and architectures for implementing ML at scale using HPC.
Machine Learning (ML) and High Performance Computing (HPC) on AWS run compute intensive workloads across industries and emerging applications. It’s use cases can be linked to various verticals like computational fluid dynamics (CFD), genomics, and autonomous vehicles.
The book provides end-to-end guidance starting from HPC concepts for storage and networking. It then goes deeper into part 2, with working examples on how to process large datasets using SageMaker Studio and EMR, build, train, and deploy large models using distributed training. It also covers 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 will be able to build, train, and deploy your own large scale ML application, using HPC on AWS, following the industry best practices and addressing the key pain points encountered in the application life cycle.
What you will learn
- Data management, storage, and fast networking for HPC applications
- Analysis and visualization of a large volume of data using Spark
- Train visual transformer model using SageMaker distributed training
- Deploy and manage ML models at scale on cloud and at edge
- Performance optimization of ML models for low latency workloads
- Apply HPC to industry domains like CFD, genomics, AV, and optimization