Data Quality Monitoring Advanced

Bad data is the number one cause of ML model failures in production. Data quality monitoring catches issues at the source-before they propagate through your pipeline and degrade model performance. This lesson covers schema validation, statistical testing, distribution drift detection, and data lineage tracking.

Data Quality Checks

• Schema validation - Verify column names, data types, and required fields match expectations
• Completeness checks - Detect missing values, null rates, and empty fields beyond acceptable thresholds
• Range checks - Ensure numerical values fall within expected ranges (e.g., age between 0-120)
• Uniqueness checks - Verify primary keys are unique and referential integrity is maintained
• Freshness checks - Confirm data timestamps are within expected recency windows

Distribution Drift Detection

Data distribution drift is when the statistical properties of input data change over time. Detecting drift early prevents silent model degradation:

Great Expectations Integration

import great_expectations as gx

context = gx.get_context()
validator = context.sources.pandas_default.read_csv("training_data.csv")

# Define expectations
validator.expect_column_values_to_not_be_null("user_id")
validator.expect_column_values_to_be_between("age", min_value=0, max_value=120)
validator.expect_column_mean_to_be_between("purchase_amount", min_value=10, max_value=500)

results = validator.validate()
if not results.success:
    alert_data_quality_failure(results)

Data Lineage Tracking

Track the origin and transformation history of every piece of data in your ML pipeline:

• Source tracking - Record which data sources contributed to each training dataset
• Transformation audit - Log every transformation applied to the data with parameters
• Version linkage - Connect dataset versions to model versions for reproducibility
• Impact analysis - When a data source has issues, identify which models are affected