Life Science — AI Model Lineage Tracking Pipeline

Overview

The purpose of this DAG is to provide comprehensive traceability for AI models within the life sciences sector, ensuring that their evolution and utilization are meticulously documented over time. It ingests data from diverse model management systems, including model performance logs, version control records, and compliance checklists. The ingestion pipeline begins with data extraction from these sources, followed by transformation processes that standardize and enrich the data for analysis. Key

The purpose of this DAG is to provide comprehensive traceability for AI models within the life sciences sector, ensuring that their evolution and utilization are meticulously documented over time. It ingests data from diverse model management systems, including model performance logs, version control records, and compliance checklists. The ingestion pipeline begins with data extraction from these sources, followed by transformation processes that standardize and enrich the data for analysis. Key processing steps involve validating model access logs, conducting compliance tests, and generating lineage reports that detail the history and usage of each model. Quality controls are integral to the process, with automated checks for access validation and compliance testing to ensure adherence to industry regulations. The outputs of this DAG include detailed lineage reports, compliance audit trails, and metrics on model tracking, which are essential for regulatory submissions and internal audits. Monitoring KPIs, such as the number of models traced and report generation time, provide insights into the efficiency and effectiveness of the lineage tracking process. By implementing this DAG, organizations in the life sciences can enhance their governance frameworks, improve regulatory compliance, and foster trust in their AI-driven processes.

Part of the Enterprise Search solution for the Life Science industry.

Use cases

• Ensures compliance with industry regulations and standards
• Enhances transparency in AI model usage and evolution
• Reduces time and effort in generating compliance reports
• Improves trust in AI-driven decision-making processes
• Facilitates better governance and risk management