data-pipeline

Automate data preprocessing by loading raw data sources, profiling dataset quality, and applying modality-specific cleaning and transformation steps. This pipeline converts NLP and tabular inputs into model-ready datasets through structured validation and reproducible reporting.

Run model evaluation by supplying the trained model, test dataset, and required metrics. The pipeline harnesses these evaluation artifacts to compute metrics and generate reproducible reports across NLP, tabular, and mixed modalities.

Yes, you can perform API load testing by providing the target endpoints. The pipeline executes performance testing to validate scalability and monitors API behavior under load to ensure system reliability.

Generate a data quality profile by ingesting raw data into the pipeline. It profiles the dataset to assess quality, applies cleaning transformations, and outputs a structured quality report to monitor data health.

Required inputs include data sources, modality type, task type, evaluation artifacts like models and test datasets, target metrics, and load-testing endpoints. These inputs configure the automated data preparation, evaluation, and testing workflows.

Yes, the data pipeline enforces reproducible workflows by structuring profiling, cleaning, validation, reporting, and monitoring steps. This ensures consistent data transformation and API performance testing outputs across multiple runs.