Checking data to make sure it follows the right format and meets expected rules before it is used — like confirming that a date field actually contains a valid date.
Checking data to make sure it follows the right format and meets expected rules before it is used — like confirming that a date field actually contains a valid date.
Data validation operates at the point of data entry, ingestion, or transformation and is distinct from data verification: validation checks conformance to predefined rules (schema, range, format, referential integrity), while verification checks accuracy against an external reference. In AI pipelines, validation is applied to raw datasets before training to catch malformed records, out-of-range values, referential inconsistencies, and schema violations. Validation rules should be codified and versioned alongside training code so that any future data ingestion is subject to the same checks. ISO/IEC 5259 Part 4 covers data quality processes including validation within AI data pipelines.
Automated validation gates in AI training pipelines prevent invalid data from silently corrupting model training — a standard engineering practice that significantly reduces debugging costs.
Like passport control at an airport — it checks whether a document conforms to expected standards (correct format, not expired, biometric chip readable) but does not independently verify that the person is who they claim to be.
Checking data to make sure it follows the right format and meets expected rules before it is used — like confirming that a date field actually contains a valid date.
Data validation operates at the point of data entry, ingestion, or transformation and is distinct from data verification: validation checks conformance to predefined rules (schema, range, format, referential integrity), while verification checks accuracy against an external reference. In AI pipelines, validation is applied to raw datasets before training to catch malformed records, out-of-range values, referential inconsistencies, and schema violations. Validation rules should be codified and versioned alongside training code so that any future data ingestion is subject to the same checks. ISO/IEC 5259 Part 4 covers data quality processes including validation within AI data pipelines.
Automated validation gates in AI training pipelines prevent invalid data from silently corrupting model training — a standard engineering practice that significantly reduces debugging costs.