Feature Engineering

Real-time feature store pipeline with Kinesis and SageMaker

Stream features directly into SageMaker Feature Store for low-latency ML inference.

What this covers

Deploy streaming infrastructure that captures, processes, and serves features in real-time for online ML applications requiring sub-second response times.

Implementation trail

Kinesis stream configuration
Lambda feature processing
SageMaker Feature Store setup
Online/offline store synchronization
Performance monitoring and scaling

Design the streaming feature architecture

Configure Kinesis Data Streams with appropriate shard count based on expected throughput and partition key distribution.
Set up Lambda functions to process incoming feature data with proper error handling and dead letter queues.
Enable stream encryption and configure retention periods to meet compliance requirements.

Implement feature processing logic

Transform raw events into feature store format with consistent schema validation.
Add event timestamps and entity identifiers required by SageMaker Feature Store.
Implement batch processing to optimize throughput and reduce API calls to the feature store.

Configure SageMaker Feature Store

Define feature groups with appropriate online and offline store configurations.
Set up proper IAM roles for Lambda functions to write to both online and offline stores.
Configure S3 storage for offline features with partitioning for efficient querying.

Monitor and scale the pipeline

Set up CloudWatch metrics for stream throughput, Lambda duration, and feature store write rates.
Configure auto-scaling for Kinesis shards based on incoming data volume.
Implement alerting for processing failures and feature store write errors.

Need real-time ML features?

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