Scale Your Large Training Jobs with Data and Model Parallelism

In the past, accessing a graphic processing unit (GPU) to accelerate your data processing or scientific simulation code was difficult. Today, startups can log on to their AWS console and choose from a range of GPU-based Amazon EC2 instances on demand. Whether you're looking for a do-it-yourself or a fully managed approach, we'll show you how to choose the right instance on AWS to meet your target performance goals. You'll learn how to maximize resource utilization to find performance bottlenecks, and how to reduce overall training and inference costs.

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How to Accelerate Your Models to Production with Amazon SageMaker
How to Accelerate Your Models to Production with Amazon SageMaker

Dive deep into demonstrating SageMaker’s advanced features that help you train and iterate on your ML model...

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Managed Blockchain at AWS for Startups
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