Real-time interfaces

Here are the key steps to deploy real-time interfaces in SageMaker:

1. Prepare Model

• Package your trained model with inference code

• Create model.tar.gz containing the model artifacts and inference.py script

1. Create SageMaker Model

• Use CreateModel API/console to register your model

• Specify container image and model artifacts location in S3

1. Create Endpoint Configuration

• Define instance type (e.g. ml.t2.medium)

• Set number of instances for scaling

• Configure production variants if A/B testing

1. Create Endpoint

• Deploy using the endpoint configuration

• Wait for status to become "InService"

• Get endpoint name/URL for making predictions

1. Invoke Endpoint

• Use InvokeEndpoint API for real-time predictions

• Send properly formatted input data

• Get predictions in response

The minimum required components are:

# Basic deployment flow
model = Model(
    model_data='s3://bucket/model.tar.gz',
    image_uri=container_uri,
    role=role)

predictor = model.deploy(
    instance_type='ml.t2.medium',
    initial_instance_count=1)