Data Quality and Validation

Inconsistent, incomplete, or incorrect data leads to failures in CI/CD pipelines or inaccurate models; and real-time validation at scale is complex. Poor quality data affects application reliability and model predictions.

One of the challenge is: hidden cost of poor data quality on machine learning model predictions and sometimes Application Reliability

• Inconsistent data undermines model accuracy, leading to flawed predictions.

• Data drift over time can degrade model performance unnoticed.

• Incomplete data creates blind spots that erode decision-making reliability.

The Data Engineering platform assists implement data quality checks, schema validation, and anomaly detection in pipelines. It helps to identify and address these challenges with robust data validation pipelines, anomaly detection, and automated monitoring to ensure models operate with reliable inputs through Data Engineering platform.