MLREL03-BP02 Verify feature consistency across training and inference
Provide consistent, scalable, and highly available features between training and inference using a feature storage. This results in reducing the training-serving skew by keeping feature consistency between training and inference.
Desired outcome: You create a centralized feature repository where feature definitions are stored, versioned, and shared across your organization. This makes the same features used during model training consistently available during inference, which reduces training-serving skew. You can discover, reuse, and share features across different ML projects, reducing duplicate work and standardizing feature engineering practices.
Common anti-patterns:
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Recreating feature transformations separately for training and inference pipelines.
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Storing features in different formats or locations for training versus production.
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Lack of versioning for features, leading to inconsistencies when models are updated.
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Duplicating feature engineering work across different teams or projects.
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Using non-standardized approaches to feature storage and retrieval.
Benefits of establishing this best practice:
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Reduces training-serving skew, leading to more reliable model performance in production.
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Increases developer productivity through feature reusability.
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Standardizes feature definitions across the organization.
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Improves model governance and auditability through feature versioning.
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Saves costs by avoiding redundant feature computation and storage.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Feature consistency between training and inference is critical for machine learning system reliability. When the features used to train a model differ from those used during inference, model performance can degrade, a problem known as training-serving skew. To avoid this issue, you need a centralized feature repository that provides consistent access to the same feature definitions and transformations across both training and inference environments.
A feature store serves as this centralized repository, enabling you to define, store, and retrieve features consistently. It provides mechanisms for versioning features, which verifies that you can maintain compatibility between models and the features they expect as your data and feature engineering processes evolve. Additionally, a feature store allows for the sharing and reuse of features across multiple ML projects, increasing efficiency and standardizing feature engineering practices across your organization.
Implementation steps
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Set up Amazon SageMaker AI Feature Store. Create and configure a SageMaker AI Feature Store
to serve as your centralized repository for ML features. SageMaker AI Feature Store provides both online and offline storage capabilities: the online store supports low-latency, real-time inference use cases, while the offline store supports training and batch inference processes. Create a feature group that defines your feature structure, data types, and storage configurations using the SageMaker AI SDK or console. -
Define feature groups and schemas. Organize your features into logical groups based on business domains, data sources, or ML use cases. Define schemas for your features, including data types, descriptions, and metadata. This organization makes features more discoverable and more straightforward to manage across your organization.
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Implement feature ingestion pipelines. Build automated pipelines to ingest and process raw data into features. Use SageMaker AI Processing, AWS Glue
, or Amazon EMR to transform raw data into feature values. Configure both batch ingestion for historical data and streaming ingestion for real-time updates using services like Amazon Kinesis with SageMaker AI Feature Store. -
Develop feature retrieval mechanisms. Create standardized ways to retrieve features for both training and inference. For training datasets, implement code that pulls features from the offline store, while for inference, develop services that query the online store. Verify that both paths use the same feature definitions and transformations.
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Integrate with ML workflows. Connect your feature store to your ML pipelines by integrating it with SageMaker AI Pipelines
or your custom ML workflows. This makes feature retrieval consistent throughout the ML lifecycle, from experimentation to production deployment. -
Monitor and validate features. Implement monitoring for your feature store to detect data drift, missing values, or other quality issues. Use SageMaker AI Model Monitor or custom validation scripts for feature consistency and quality over time. Set up alerts for deviations in feature distributions.
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Enable feature discovery and sharing. Document your features with metadata and descriptions to make them discoverable across your organization. Integrate with data catalogs like AWSAWS Glue Data Catalog to enhance discoverability and governance of features.
Resources
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