Why SageMaker Model Deployment?
Amazon SageMaker makes it easy to deploy ML models including foundation models (FMs) to make inference requests at the best price-performance for any use case. From low latency (a few milliseconds) and high throughput (millions of transactions per second) to long-running inference for use cases such as natural language processing and computer vision, you can use SageMaker for all their inference needs. SageMaker is a fully managed service and integrates with MLOps tools, so you can scale your model sagemaker-deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden.
Benefits of SageMaker Model Deployment
Wide range of options for every use case
Broad range of inference options
From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.
Real-Time Inference
Low latency and ultra-high throughput for use cases with steady traffic patterns.
Serverless Inference
Low latency and high throughput for use cases with intermittent traffic patterns.
Asynchronous Inference
Low latency for use cases with large payloads (up to 1 GB) or long processing times (up to 15 minutes).
Scalable and cost-effective deployment options
Amazon SageMaker provides scalable and cost-effective ways to deploy large numbers of ML models. With SageMaker’s multiple models on a single endpoint, you can deploy thousands of models on shared infrastructure, improving cost-effectiveness while providing the flexibility to use models as often as you need them. Multiple models on a single endpoint support both CPU and GPU instance types, allowing you to reduce inference cost by up to 50%
Single-model endpoints
One model on a container hosted on dedicated instances or serverless for low latency and high throughput.
