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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Don’t let the idea of integrating artificial intelligence (AI) or machine learning (ML) into your workflow intimidate you.
Watch this session to review AWS AV solutions for the toolchain including data ingest, management, labeling, simulation, and model training.
To help learn from those who’ve done it before, we’ve gathered AI/ML founders from some of the world’s top startups to give a peek behind the scenes into the secrets of their own success.
Discover how to set up a data lake and implement it into an ML experiment workflow, how to prepare an end-to-end workflow to easily share the workload, and other tips for scaling your startup.
Dive deep into demonstrating SageMaker’s advanced features that help you train and iterate on your ML models faster.