MLCOST03-BP04 Enable feature reusability
Reduce duplication and the rerunning of feature engineering code across teams and projects by using feature storage. The store should have online and offline storage, and data encryption capabilities. An online store with low-latency retrieval capabilities is ideal for real-time inference. An offline store maintains a history of feature values and is suited for training and batch scoring.
Desired outcome: You gain a centralized repository for storing, sharing, and managing machine learning features that reduces redundant work across teams and projects. You access features with low latency for real-time applications while maintaining a historical record for training purposes. Your feature store integrates seamlessly with your ML workflows, enhancing collaboration and accelerating model development while maintaining data security through robust encryption.
Common anti-patterns:
-
Recreating the same features repeatedly across different teams and projects.
-
Storing features in isolated data silos that avoid reuse.
-
Lacking version control for features, leading to inconsistencies between training and inference.
-
Using separate systems for real-time and batch feature access.
-
Implementing homegrown feature storage solutions that lack scalability and proper governance.
Benefits of establishing this best practice:
-
Reduces redundant work and computational costs.
-
Creates consistency between training and inference environments.
-
Enables collaboration and knowledge sharing across teams.
-
Provides feature governance, lineage, and traceability.
-
Reduces time to production for ML models.
Level of risk exposed if this best practice is not established: Medium
Implementation guidance
Feature engineering is often one of the most time-consuming aspects of machine learning development. When teams work in silos, they frequently recreate the same features, wasting valuable time and resources. By implementing a centralized feature store, you create a single source of truth for ML features that promotes reusability across your organization.
A well-designed feature store addresses the dual requirements of offline storage for training and batch inference and online storage for low-latency real-time inference. This dual-storage paradigm creates consistency between training and serving environments while optimizing for different access patterns. The feature store should also provide capabilities for feature versioning, access control, and monitoring to maintain data quality and governance.
Amazon SageMaker AI Feature Store offers these capabilities as a fully managed service, which reduces the need to build and maintain complex infrastructure. It seamlessly integrates with your ML pipelines and supports both batch and real-time inference workflows, making it an ideal solution for feature reusability.
Implementation steps
-
Identify common features across projects. Begin by analyzing your existing ML workflows to identify frequently used features that would benefit from centralization. Look for redundancies in feature engineering code across different teams and prioritize these for migration to the feature store.
-
Set up Amazon SageMaker AI Feature Store. Create feature groups in Amazon SageMaker AI Feature Store to organize related features. Define the schema for each feature group, including feature names, data types, and primary keys. Consider the access patterns for both training and inference when designing your feature groups.
-
Configure storage options based on requirements. Determine whether each feature group needs online storage, offline storage, or both. Configure the appropriate storage options:
-
Online store: Set up for low-latency access (milliseconds) needed for real-time inference
-
Offline store: Configure Amazon S3 storage for training and batch inference workloads
-
Online and offline: Implement both for maximum flexibility
-
-
Implement data ingestion pipelines. Develop automated pipelines to ingest data into your feature store. You can use Amazon SageMaker AI Data Wrangler
for data preparation and Amazon SageMaker AI Pipelines for orchestration. -
Establish feature access patterns. Create standardized methods for retrieving features for both training and inference. For training, use the offline store with Amazon Athena queries to efficiently access historical data. For real-time inference, implement API calls to the online store for low-latency feature retrieval.
-
Enable cross-account and cross-team sharing. Configure resource policies to enable feature sharing across different teams and AWS accounts. This promotes collaboration and maximizes feature reuse across your organization while maintaining appropriate access controls.
-
Implement feature versioning and lineage tracking. Track changes to features over time using versioning capabilities. Link features to models through Amazon SageMaker AI Model Registry to maintain full lineage tracking from data source to deployed model.
-
Monitor feature usage and drift. Implement monitoring for your feature store to detect data drift and track feature usage patterns. Use Amazon SageMaker AI Model Monitor to detect changes in feature distributions that might impact model performance.
-
Create documentation and discovery mechanisms. Document features and their intended use cases to facilitate discovery and reuse. Implement tagging and search capabilities so that data scientists can find relevant features for their projects.
-
Use enhanced Feature Store capabilities. Use improved SageMaker AI Feature Store with better performance, enhanced monitoring capabilities, and improved integration with other SageMaker AI services for more efficient feature management.
-
Use generative AI for feature discovery and documentation. Use large language models through Amazon Bedrock
to automatically generate feature descriptions, identify potential feature relationships, and improve feature discoverability through natural language search capabilities.
Resources
Related documents:
Related examples:
Related videos:
