A central operational challenge for feature stores is maintaining data integrity across different environments and over time. Ensuring that features used for model training are consistent with those used for online inference is necessary for reliable model performance. This chapter presents practical strategies for managing data consistency and quality.

You will learn to:

• Identify and mitigate online/offline skew, where feature distributions differ, sometimes denoted as $P_{train}(X) \neq P_{serve}(X)$.
• Implement point-in-time correct feature lookups essential for generating accurate training datasets.
• Apply sophisticated data validation rules during feature ingestion.
• Monitor feature distributions to detect shifts or drift over time.
• Understand approaches and complexities of backfilling historical feature data.
• Analyze the implications of different consistency guarantees (e.g., eventual vs. strong) in distributed feature store setups.