Data Curation

Bytes Transformed Into Gold

Data Curation involves identifying, collecting, and preparing relevant data sources for model development. This stage includes data cleaning, preprocessing, and transformation to ensure data quality, completeness, and consistency. Data from multiple sources is integrated, and inconsistencies are resolved to create a unified dataset. Data pipelines are created and maintained for automated data ingestion, and data versioning and tracking mechanisms are implemented. Collaboration with domain experts is essential for data validation and annotation. Data Curation is a critical step in ensuring the model is trained on high-quality and reliable data.

  • Source Identification, Assessment, and Collection
  • Integration, Cleaning, and Preprocessing
  • Transformation and Feature Engineering
  • Pipeline Development and Automation
  • Versioning, Tracking, and Validation
  • Governance and Security