High Tech — AI Model Lineage Tracking for Compliance

Overview

The primary purpose of this DAG is to track the lineage of AI models, ensuring compliance with industry regulations and internal standards. It is triggered upon the creation or update of an AI model, collecting lineage information from various model management systems. The ingestion pipeline begins with the retrieval of model metadata, which includes versioning, training data sources, and deployment details. Following this, data validation is performed to ensure the integrity and accuracy of the

The primary purpose of this DAG is to track the lineage of AI models, ensuring compliance with industry regulations and internal standards. It is triggered upon the creation or update of an AI model, collecting lineage information from various model management systems. The ingestion pipeline begins with the retrieval of model metadata, which includes versioning, training data sources, and deployment details. Following this, data validation is performed to ensure the integrity and accuracy of the lineage information. The next step involves updating lineage reports, which consolidate the collected data into a structured format. Quality control measures are implemented throughout the process, including checks for data consistency and completeness, with alerts generated for any discrepancies identified. The final steps include generating dashboards that provide stakeholders with visual insights into model lineage, facilitating better decision-making. Key performance indicators (KPIs) for monitoring include the number of models tracked, the frequency of updates, and the accuracy of lineage information. This DAG provides significant business value by enhancing compliance with regulatory requirements, improving transparency in AI model management, and fostering trust among stakeholders.

Part of the Enterprise Search solution for the High Tech industry.

Use cases

• Enhances regulatory compliance and reduces risk exposure
• Increases transparency in AI model management processes
• Facilitates informed decision-making for stakeholders
• Improves trust and accountability in AI systems
• Streamlines reporting processes and reduces manual efforts