A retail company is using an Amazon Redshift cluster to support real-time inventory management.

The company needs to use machine learning models for real-time inventory recommendations and future inventory predictions while leveraging both Amazon Redshift and Amazon SageMaker.

Option A: Use Amazon Redshift ML to generate inventory recommendations.Amazon Redshift MLallows you to build, train, and deploy machine learning models directly from Redshift using SQL statements. It integrates with SageMaker to train models and run inference. This feature is useful for generating inventory recommendations directly from the data stored in Redshift.

Option B: Use SQL to invoke a remote SageMaker endpoint for prediction.You can use SQL in Redshift to call aSageMaker endpointfor real-time inference. By invoking a SageMaker endpoint from within Redshift, the company can get real-time predictions on inventory, allowing for integration between the data warehouse and the machine learning model hosted in SageMaker.

Option C (offline model training)andOption D (creating dashboards with SageMaker Autopilot)are not relevant to the real-time prediction and recommendation requirements.

Option E (archiving inventory reports in Redshift)is not related to making predictions or recommendations.