MLPERF06-BP06 Review for updated data and features for retraining
Establishing a framework to regularly review and update your machine learning model's data and features is essential for maintaining model accuracy. As business environments evolve, new data patterns emerge that can impact your model's performance. By systematically reviewing your data and features at appropriate intervals, you can keep your models accurate and reliable.
Desired outcome: You establish a systematic approach to monitor data changes, explore new features, and incorporate updated data into your models. Through regular data exploration and feature engineering, you maintain model accuracy even as underlying data patterns evolve. This creates a proactive rather than reactive approach to model maintenance and verifies that your ML solutions consistently deliver business value.
Common anti-patterns:
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Assuming that data patterns remain stable over time.
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Retraining models only when performance degrades.
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Failing to explore new potential features as business evolves.
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Using the same feature engineering approach regardless of changing data characteristics.
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Not establishing regular review schedules for data and feature updates.
Benefits of establishing this best practice:
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Improved model accuracy through updated training data and features.
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Early detection of data drift and proactive model updates.
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Continuous discovery of new, potentially valuable features.
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Consistent model performance despite changing business conditions.
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Extended model lifecycle and reduced need for complete rebuilds.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Data is the foundation of a machine learning model, and its characteristics can change over time due to various factors such as seasonal variations, market shifts, or changes in customer behavior. Without a framework to regularly review and update your data and features, models can gradually become less accurate as they fail to account for these changes.
Avoid assuming that data patterns remain stable over time. Many organizations retrain models only when performance degrades, fail to explore new potential features as their business evolves, and use the same feature engineering approach regardless of changing data characteristics.
To implement this practice effectively, you need to understand the volatility of your business environment and establish appropriate review intervals. For example, retail businesses might need more frequent reviews during holiday seasons when consumer behavior changes rapidly. You also need tools to efficiently explore data, identify new patterns, and engineer features that capture these insights.
Amazon SageMaker AI provides comprehensive capabilities for data preparation, feature engineering, and model monitoring. By using these tools, you can create an efficient pipeline for regularly reviewing and updating your model's data and features, providing continued accuracy and relevance.
Implementation steps
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Assess data volatility in your business environment. Analyze how quickly your business data changes by examining historical data patterns and identifying seasonal trends, market shifts, or other factors that affect your data. This assessment can determine how frequently you need to review your model's data and features.
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Establish a review schedule. Based on your data volatility assessment, create a calendar for regular data and feature reviews. For highly volatile environments, you may need monthly reviews, while more stable contexts might require quarterly or biannual reviews.
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Set up data monitoring. Implement Amazon SageMaker AI Model Monitor to continuously track data drift by comparing production data against your model's training data. Configure alerts when deviations occur to run expedited reviews.
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Create a data exploration workflow with Amazon SageMaker AI Canvas. Use Amazon SageMaker AI Canvas to build data exploration workflows. The unified SageMaker AI Studio environment provides seamless integration with S3, Redshift, and EMR for comprehensive data exploration, engineering, training, and deployment workflows. Canvas now includes enhanced no/low-code ML tools with templates, automation, and wizards that enable non-engineering users to train custom models for verticals like sales, fraud, and demand with minimal technical expertise. These workflows should include data visualizations, statistical analyses, and data quality assessments.
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Implement feature engineering processes. Develop standardized feature engineering pipelines in SageMaker AI Data Wrangler that can transform raw data into model features. Include steps to identify potential new features during each review cycle.
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Integrate with SageMaker AI Feature Store. Store engineered features in Amazon SageMaker AI Feature Store to maintain feature consistency between training and inference. This creates a single source of truth for features and simplifies retraining with updated data.
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Establish an evaluation framework. Create a systematic approach to compare model performance using original features versus updated or new features. This quantifies the impact of feature changes and supports data-driven decisions about model updates.
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Form a cross-functional review team. Assemble a team including data scientists, domain experts, and business stakeholders who can collectively evaluate data changes, validate new features, and authorize model retraining when necessary.
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Document changes and maintain version control. Track changes to data sources, feature definitions, and transformation logic using version control systems. This creates an audit trail and supports reproducibility.
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Automate the retraining pipeline. Use Amazon SageMaker AI Pipelines to create automated workflows that can retrain models with updated data and features when approved by the review team.
Resources
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