AWS Podcast
#551: Deep Dive Into SageMaker Serverless Inference
Oct 17, 2022
Rishabh Ray Chaudhury, a Senior Product Manager at AWS with expertise in SageMaker, delves into the innovative SageMaker Serverless Inference. He discusses how this feature alleviates infrastructure headaches, allowing users to focus on their machine learning models. Rishabh highlights customer use cases, emphasizing cost savings and ease of deployment. The conversation also covers efficient model updates and real-world success stories, showcasing the significant benefits of using serverless architecture for machine learning applications.
16:15
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Quick takeaways
- SageMaker Serverless Inference enables automatic scaling based on traffic patterns, reducing the need for manual resource management and optimizing cost-efficiency.
- This feature particularly benefits applications with unpredictable traffic, exemplified by significant cost reductions observed in customer use cases like a payroll chatbot service.
Deep dives
Introduction to SageMaker Serverless Inference
Amazon SageMaker serverless inference is a newly launched feature that allows users to deploy machine learning models for inference without managing underlying infrastructure. This serverless option scales automatically based on traffic patterns, eliminating the need for customers to adjust scaling policies manually. Users simply provide the location of their inference code and model artifacts, while SageMaker handles all associated infrastructure. This functionality enables customers to concentrate on optimizing their machine learning code rather than getting bogged down in infrastructure management.
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