AWS Feature Store
Commercial Information
| Vendor Name | AWS Feature Store |
| History | Developed internally by AWS |
| Stand-alone vs. Platform | Part of the Amazon SageMaker platform |
| Delivery Model | Fully-managed cloud service |
| Clouds Supported | AWS |
| Pricing Model | Consumption Pricing |
| Service Level Guarantees | None |
| Support | 24 x 7 support & response time guarantees |
Feature Store Capabilities
| Feature Definitions | Not available |
| Automated Transforms | Not available, requires setting up transformations using Data Wrangler or Glue Databrew, and setting up pipelines with SageMaker Pipelines or Airflow |
| Feature Ingestion | Batch ingestion with Spark or ingestion API into offline & online store Streaming ingestion with Spark Streaming or ingestion API into offline & online store |
| Storage and Feature Processing Infrastructure | Online storage: DynamoDB Offline storage: S3 |
| Feature Sharing and Discovery | Web UI |
| Training Dataset Generation | Dataset generated from offline storage using AWS SDK |
| Online Serving | Serving endpoint / API for online data |
| Monitoring and Alerting | Not available |
| Security and Data Governance | ACL and RBAC SSO Data encryption at rest and in flight |
| Integrations | Batch data: S3, Athena, Redshift Streaming data: Any streaming source |
Amazon SageMaker Feature Store
What Data Sources Can Amazon SageMaker Feature Store Ingest From?
Amazon Kinesis Data Firehose. You can also create features in data preparation tools such as Amazon SageMaker Data Wrangler, and store them directly into SageMaker Feature Store.
Does Amazon SageMaker Feature Store Support Streaming Data Sources?
Streaming data is fed into SageMaker Feature via a synchronous PutRecord API. This requires buildout to make successful streaming ingestion and adds a point of failure to an ingestion pipeline.
Does AWS SageMaker Feature Store Have A Web UI?
The primary web UI for the SageMaker Feature Store is a notebook.
How Does AWS SageMaker Feature Store Manage Batch Data Transformations?
Sagemaker uses Data Wrangler and Spark to ingest data into the feature store, these are scheduled via lambda functions.
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Commercial Information
| Vendor |
| Demo link |
| History |
| Stand-alone vs. Platform |
| Delivery Model |
| Clouds Supported |
| Pricing Model |
| Service Level Guarantees |
| Support |
Feature Store Capabilities
| Feature Definitions |
| Automated Transforms |
| Feature Ingestion |
| Storage and Feature Processing Infrastructure |
| Feature Sharing and Discovery |
| Training Dataset Generation |
| Online Serving |
| Monitoring and Alerting |
| Security and Data Governance |
| Integrations |