Defense & Aerospace — Model Registry for Compliance and Traceability

Overview

The purpose of this DAG is to create a robust model registry that tracks and manages AI models utilized in Defense and Aerospace applications. It ingests metadata from various development environments, including model configurations, performance metrics, and compliance status. The ingestion pipeline begins with the collection of model metadata from repositories and development logs, followed by a series of processing steps that validate and standardize the data. Quality control measures are impl

The purpose of this DAG is to create a robust model registry that tracks and manages AI models utilized in Defense and Aerospace applications. It ingests metadata from various development environments, including model configurations, performance metrics, and compliance status. The ingestion pipeline begins with the collection of model metadata from repositories and development logs, followed by a series of processing steps that validate and standardize the data. Quality control measures are implemented to ensure that all models meet established compliance standards, with automated checks for adherence to regulatory requirements. The processed data is then stored in a secure data warehouse, which serves as the foundation for reporting and auditing activities. Outputs include detailed compliance reports and alerts for non-compliant models, which are essential for maintaining operational integrity. Monitoring KPIs such as model compliance rates, audit frequency, and alert response times are established to ensure ongoing oversight. The business value of this DAG lies in its ability to enhance accountability, streamline audits, and mitigate risks associated with non-compliance in the highly regulated Defense and Aerospace sector.

Part of the Enterprise Search solution for the Defense & Aerospace industry.

Use cases

• Enhances compliance with stringent Defense and Aerospace regulations.
• Reduces risk of operational disruptions due to non-compliance.
• Streamlines audit processes, saving time and resources.
• Improves accountability in AI model management.
• Facilitates informed decision-making with accurate reporting.