Amazon SageMaker AI
A managed machine learning platform under Amazon AWS that provides capabilities for model training, deployment, iteration, and online learning. It supports enterprise-scale implementation of reinforcement learning, contextual bandit algorithms, and related approaches for production use cases.
Core Facts
Timeline (last 90 days)
Muse Spark Open's weight download channels include Amazon SageMaker AI, Google Cloud Model Garden, Hugging Face and Microsoft Foundry Models.
The AWS engineering team carried out kernel-level optimizations on Amazon SageMaker AI for a fused MoE model, including Swap-AB to improve WGMMA utilization, Triton backend routing to implement FP8 quantization, and eliminating repeated Triton JIT compilation.
These optimizations brought up to a 10% throughput improvement and up to a 9% improvement in time to first token (TTFT) on H200 GPUs.
The three kernel optimizations can be orthogonally stacked with upper-layer optimizations, and for teams deploying MoE models on SageMaker AI, these optimizations will likely be reflected in service performance in a managed form.
The Amazon Payments team used multi-objective contextual bandits on Amazon SageMaker AI to personalize the customer acquisition funnel, achieving close to a double-digit conversion rate improvement on a specific audience segment.
The core of this solution is multi-turn reinforcement learning (MTRL), leveraging Amazon SageMaker AI to complete the end-to-end training pipeline.
The managed training environment provided by SageMaker AI can reduce the engineering complexity of building an RL pipeline.
CPU-GPU hybrid inference (offloading) introduces a significant drop in inference speed.
All Facts (7)
CPU-GPU hybrid inference (offloading) introduces a significant drop in inference speed.
75%UnverifiedThese optimizations brought up to a 10% throughput improvement and up to a 9% improvement in time to first token (TTFT) on H200 GPUs.
50%UnverifiedThe AWS engineering team carried out kernel-level optimizations on Amazon SageMaker AI for a fused MoE model, including Swap-AB to improve WGMMA utilization, Triton backend routing to implement FP8 quantization, and eliminating repeated Triton JIT compilation.
50%UnverifiedThe Amazon Payments team used multi-objective contextual bandits on Amazon SageMaker AI to personalize the customer acquisition funnel, achieving close to a double-digit conversion rate improvement on a specific audience segment.
50%UnverifiedThe managed training environment provided by SageMaker AI can reduce the engineering complexity of building an RL pipeline.
50%UnverifiedThe core of this solution is multi-turn reinforcement learning (MTRL), leveraging Amazon SageMaker AI to complete the end-to-end training pipeline.
50%UnverifiedThe three kernel optimizations can be orthogonally stacked with upper-layer optimizations, and for teams deploying MoE models on SageMaker AI, these optimizations will likely be reflected in service performance in a managed form.
50%