validation-scripts

Run data validation scripts to check datasets against schemas, detect duplicates, and verify token counts for model compatibility. This prevents training-time failures by catching data quality issues early with structured reports and recommended fixes.

Pipeline testing runs end-to-end checks from data ingestion to inference to catch integration faults early. You need it when validating ML workflows to ensure pipelines execute completely without breaking during training or inference stages.

Yes, dependency verification scripts check for required packages like PyTorch, Transformers, pandas, and pyarrow. They ensure required tools are present before training, generating actionable reports with recommended fixes for any missing components.

Run the test-pipeline.sh script to validate the complete ML workflow from data ingestion to inference. This catches integration faults early and provides structured reports to fix pipeline reliability issues before training begins.

Data validation scripts using pandas and pyarrow perform schema checks, duplicate detection, and token validation across ML datasets. They handle data-quality validation tasks, providing structured reports and recommended fixes for any issues found.

Implement validation scripts for schema checks, duplicate detection, and dependency verification before training. This ensures clean data and tested workflows, catching integration faults and quality issues early with structured actionable reports.