Select the Right ML Instance for your Training and Inference Job (Level 300)

June 9, 2021

AWS offers a breadth and depth of machine learning (ML) infrastructure for training and inference workloads that you can use through either a do-it-yourself approach or a fully managed approach with Amazon SageMaker. In this session, explore how to choose the proper instance for ML training and inference based on model size, complexity, throughput, framework choice, inference latency and portability requirements. Join this session to compare and contrast compute-optimized CPU-only instances, such as Amazon EC2 C4 and C5; high-performance GPU instances, such as Amazon EC2 G4, P3, and P4d; cost-effective variable-size GPU acceleration with Amazon Elastic Inference; and high performance/cost with Amazon EC2 Inf1 instances powered by custom-designed AWS Inferentia chips.

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Detect Potential Bias in your Datasets and Explain how your Models Predict using SageMaker Clarify (Level 300)
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Automate Code Reviews, Performance Recommendations and Operational Insights  (Level 300)
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