Multi-Model Endpoint

Multi-model endpoints in AWS SageMaker allow you to deploy multiple models on a single endpoint, which can be more cost-effective and efficient than deploying each model on its own endpoint.

How It Works

A multi-model endpoint in SageMaker works by loading and unloading models into memory as needed when inference requests are made. This allows you to have many models available for inference without having to keep them all loaded in memory at once.

Benefits

• Cost-Effective: It can be more cost-effective than deploying each model on its own endpoint.
• Efficient Resource Utilization: It allows for efficient use of resources by only loading models into memory as needed.

Limitations

• Potential Latency: There can be additional latency for the first prediction request for a model, as the model needs to be loaded into memory.

Features

• Dynamic Loading and Unloading: Models are dynamically loaded and unloaded from memory as needed.
• Shared Resources: Multiple models share the same endpoint resources.

Use Cases

• Large Number of Models: It’s useful when you have a large number of models that are each used infrequently.
• Cost Optimization: It’s beneficial when you want to optimize costs for model deployment.

Product Variants

Product variants in AWS SageMaker allow you to deploy multiple versions of a model on a single endpoint for A/B testing.

How It Works

You can create multiple variants of your model, each with different weights that determine the proportion of traffic that they receive. SageMaker will then route requests to the variants based on these weights.

Benefits