Easily deploy models for the best performance and cost using Amazon SageMaker

Optimizing cloud resources to achieve the best cost and performance for your ML model is critical. In this session, learn how to use Amazon SageMaker to run performance benchmarks and load tests for inference to determine the right instance types and model optimizations.

Easily deploy models for the best performance and cost using Amazon SageMaker

Opening Keynote: Accelerate innovation with ML

Rethink possible: AI/ML innovation stories

Remove unnecessary onboarding friction with real-time fraud detection

Elevate customer experiences with AWS Contact Center Intelligence

Build unique user experiences through personalization

Extract data and insights from your documents

Adding identity verification to your application

Create real-time personalized user experiences faster at scale

Find accurate information faster with intelligent search

Generate ML predictions without writing any code

Prepare data for ML with ease, speed, and accuracy

Amazon SageMaker: Train models with tens or hundreds of billions of parameters

Use Amazon SageMaker to build high-quality ML models faster

Achieve high-performance and cost-effective model deployment

Implementing MLOps practices with Amazon SageMaker

Streamline content moderation workflows with AI/ML

Three ways ML can transform your developer operations

Choosing the right ML instances for your training and inference deployments

Build custom deep learning environments with AWS Deep Learning Containers

ML at the edge with Amazon SageMaker