Here is the summary of the video transcription in detail, broken down into sections for better readability:
Introduction
- The session is on reducing foundational model deployment costs and latencies with Amazon SageMaker.
- The presenters are Venkatesh Krishna, who leads product management for SageMaker, Amit Aurora, a Principal AI/ML Solutions Architect at AWS, and Grant Gileadi, VP and Distinguished Engineer from Capital One.
- The session will explore how to maximize return on investment by achieving optimal performance at the lowest possible cost for hosting machine learning models.
Comparing Amazon Bedrock and Amazon SageMaker
- Amazon Bedrock is best suited for use cases where turnkey deployment is preferred, with no need to worry about provisioning instances or configuring infrastructure.
- SageMaker is the go-to choice for those who need more control over their deployment infrastructure, allowing users to fully configure the managed infrastructure for their models.
SageMaker Inference Capabilities
- SageMaker offers a configurable software and hardware stack, giving customers high visibility and granular control over their machine learning infrastructure.
- SageMaker allows you to deploy multiple models onto a single endpoint, facilitating more efficient resource utilization and simplified management.
Achieving Cost-Effective Model Deployment
- SageMaker offers a powerful solution for cost-effective model deployment, including features like hosting multiple models on a single endpoint to maximize hardware utilization.
- Recent SageMaker features include:
- Faster auto-scaling, reducing over-provisioning needs.
- The ability to scale down to zero instances when there is no traffic.
- Hosting thousands of fine-tuned model flavors on the same endpoint, saving costs.
Optimizing Inference Performance
- SageMaker offers features to reduce end-to-end latency, including load-aware routing and session-aware (sticky) routing.
- The SageMaker Inference Optimization Toolkit provides technologies like speculative decoding, quantization, and compilation to speed up inference.
- Benchmarks show up to 2x improvement in throughput and 50% lower cost for large language models when using these optimizations.
Demonstration of Features
- The first demo showcased SageMaker's sticky session routing, which allows repeated inferences on the same data asset to be routed to the same instance, reducing latency.
- The second demo demonstrated SageMaker's speculative decoding feature, which uses a smaller "draft" model to generate tokens in parallel, then verifies with the larger model, reducing latency.
- The third demo showed how SageMaker's quantization feature can reduce model size and allow deployment on smaller, less expensive instances.
Capital One's Experience with SageMaker
- Capital One has been on a significant technology transformation journey, building an in-house organization of over 14,000 technologists and adopting cloud-based, API-driven, and DevOps practices.
- Capital One has AI and ML use cases in production across nearly all of their lines of business, driving value for their 100 million customers and 50,000 associates.
- To enable the use of large language models, Capital One has integrated SageMaker inference into their platform, benefiting from the reduced development time, reduced vulnerability exposure, and integration with their existing governance processes.
- Capital One is also evaluating further SageMaker optimization features like the Inference Optimization Toolkit and plan to use them to achieve additional performance and cost improvements.
Conclusion
- The session provided an in-depth look at SageMaker's inference capabilities and optimization features, as well as a real-world example of how Capital One has leveraged SageMaker for their model hosting needs.
- The audience is encouraged to provide feedback through the session survey and to reach out to the presenters with any additional questions.