The workflow for using Amazon SageMaker Feature Store follows a logical sequence. First, a feature group must be created; this acts as a container and defines the schema for the features. Second, data records are populated into this feature group through an ingestion process, typically using the PutRecord API. Finally, once the feature group contains data, it can be accessed to retrieve features and assemble a dataset for model training or to serve features for real-time inference. This sequence ensures the data structure exists before data is added, and data is present before it is used.

1. Amazon SageMaker Developer Guide, "Get Started with Amazon SageMaker Feature Store": This official guide explicitly outlines the process in this order. It details "Step 1: Create a Feature Group," followed by "Step 2: Ingest Data into your Feature Group," and concludes with "Step 3: Create a Dataset for ML."

URL: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-getting-started.html

Section: Steps 1, 2, and 3 in the "Get Started with Amazon SageMaker Feature Store" tutorial.

2. Amazon SageMaker Developer Guide, "Amazon SageMaker Feature Store": The conceptual overview page reinforces this workflow. It states, "You create a feature group... After you have created a feature group, you can add features to it... You can then use the features in the online or offline store for training or inference."

URL: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store.html

Section: "How It Works".