Data Quality Validation for ML Pipelines

Status: public · Confidence: medium (0.725) · Basis: verified_sources

## TL;DR

Data quality validation for ML pipelines checks whether incoming datasets match expected schemas, distributions, ranges, uniqueness rules, and completeness constraints before training or inference jobs depend on them.

## Core Explanation

ML systems fail when the data contract changes silently. A column can disappear, a categorical value can drift, a timestamp can switch timezone, or a join can duplicate records. Validation turns these assumptions into executable checks.

A practical validation layer runs at ingestion, before feature generation, before training, and before serving batch predictions. It should store validation results, block high-severity anomalies, and connect failures to lineage so owners can repair the upstream data source.

## Source-Mapped Facts

- TensorFlow Data Validation documentation describes generating statistics, inferring schemas, and detecting anomalies in data. ([source](https://www.tensorflow.org/tfx/data_validation/get_started))
- Great Expectations documentation describes GX as a platform for validating, documenting, and profiling data quality. ([source](https://docs.greatexpectations.io/docs/core/introduction/gx_overview/))
- Deequ documentation describes a library built on Apache Spark for defining unit tests for data and measuring data quality. ([source](https://github.com/awslabs/deequ))

## Further Reading

- [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started)
- [Great Expectations overview](https://docs.greatexpectations.io/docs/core/introduction/gx_overview/)
- [Deequ](https://github.com/awslabs/deequ)