data quality

**Data Quality** Data quality checks validate training data through schema validation distribution monitoring and anomaly detection because bad data produces bad models. Schema validation ensures correct types ranges and formats. Distribution monitoring detects drift when new data differs from training data. Anomaly detection identifies outliers duplicates or corrupted records. Checks include completeness no missing values consistency cross-field validation uniqueness no duplicates and accuracy spot-checking against ground truth. Automated validation runs on data pipelines catching issues before training. Monitoring tracks data quality metrics over time. Tools like Great Expectations Pandera and custom validators implement checks. Data quality issues cause model failures: missing values break training outliers skew learning and label errors teach wrong patterns. Prevention includes data contracts specifying expected schemas validation at ingestion and human review of samples. Data quality is often the biggest factor in model performance. Investing in data quality infrastructure pays dividends through better models and fewer production issues. Quality checks should be comprehensive automated and continuously monitored.