After training and tuning a machine learning model, it’s time to deploy it for inference. There are several deployment options, depending on your needs:
Batch vs. Real-Time Inference
- Batch Inference: Suitable for large predictions where waiting is acceptable (e.g., overnight processes). Cost-effective as resources are used periodically.
- Real-Time Inference: Needed for immediate responses, typically using a REST API for real-time interaction.
Using APIs for Model Deployment
- API: Clients send data to the model, and receive predictions via POST requests.
- Example: Amazon API Gateway can route requests to AWS Lambda, where the model is hosted.
Deployment Infrastructure
- Models can be deployed in Docker containers, which are portable across various services:
- AWS Lambda: Minimal operational overhead.
- Amazon ECS/EKS/EC2: More control over the environment.
- AWS Batch: Best for batch processing.
Using Amazon SageMaker for Inference
SageMaker provides four types of inference options:
- Batch Inference: Offline processing of large datasets, suitable for cases where immediate results aren’t necessary.
- Asynchronous Inference: Processes queued requests, ideal for large payloads or when the service can be scaled down to zero during inactivity.
- Serverless Inference: Real-time inference without managing instances, using AWS Lambda.
- Real-Time Inference: Persistent, fully managed endpoints for interactive responses, useful for sustained traffic.
These options provide flexibility to meet different business and technical needs.
