DataSchemaError
mlops.drift_detection.errors.DataSchemaError
Stack trace
mlops.drift_detection.errors.DataSchemaError: Input data schema validation failed: missing required field 'feature_vector'
File "/app/mlops/drift_detection/pipeline.py", line 87, in detect_drift
validate_schema(input_data)
File "/app/mlops/drift_detection/schema.py", line 45, in validate_schema
raise DataSchemaError(f"Input data schema validation failed: {error_msg}") Why it happens
Model drift detection systems rely on strict data schemas to validate incoming data before analysis. If the input data is missing required fields, has incorrect types, or unexpected nested structures, the schema validation raises this error. This often happens due to upstream data pipeline changes or inconsistent data formatting.
Detection
Implement schema validation logging that captures and alerts on DataSchemaError exceptions, including the raw input data snapshot to identify schema mismatches before the pipeline fails.
Causes & fixes
Input data is missing required fields like 'feature_vector' or 'timestamp'.
Ensure upstream data pipelines always include all required fields and validate data completeness before passing to drift detection.
Data types in the input do not match the schema, e.g., 'feature_vector' is a list but expected as a numpy array or dict.
Convert or cast input data fields to the expected types before schema validation, using explicit type checks and transformations.
Nested data structures have unexpected keys or missing nested fields required by the schema.
Update the data extraction or transformation logic to produce nested structures that exactly match the schema definition.
Schema definition in the drift detection code is outdated and does not reflect recent upstream data format changes.
Synchronize the schema definitions with the latest data contract from upstream sources and update validation logic accordingly.
Code: broken vs fixed
from mlops.drift_detection.pipeline import detect_drift
input_data = {
'timestamp': '2026-04-01T12:00:00Z',
# 'feature_vector' key missing here
}
# This line raises DataSchemaError due to missing 'feature_vector'
detect_drift(input_data)import os
from mlops.drift_detection.pipeline import detect_drift
from mlops.drift_detection.schema import validate_schema
input_data = {
'timestamp': '2026-04-01T12:00:00Z',
'feature_vector': [0.1, 0.2, 0.3] # Added required field
}
# Validate schema before detection to prevent errors
validate_schema(input_data)
detect_drift(input_data)
print("Drift detection ran successfully with valid schema.") # Confirm successWorkaround
Wrap the drift detection call in try/except DataSchemaError, log the raw input data for manual inspection, and apply a fallback schema correction or default values before retrying.
Prevention
Implement strict upstream data contracts with automated schema validation and type enforcement at ingestion points to guarantee consistent, schema-compliant data flows into drift detection pipelines.