Project Description
I need to turn our written AI-governance policies into a practical software toolkit that can guide, track, and report on every stage of model life-cycle management. The focus is implementation strategy: instead of advising on rules or ethics, the brief is to build a brand-new solution that embeds those rules directly into day-to-day workflows.
The core objective is a governance tool (web-based or modular API) able to:
• capture and store model documentation and approval checkpoints,
• enforce policy gates before data ingestion, training, and deployment,
• generate audit-ready logs for internal and external regulators,
• surface real-time risk metrics on model drift, bias, and performance.
I already have high-level requirements, user stories, and sample policy text; what is missing is the architecture, codebase, and an iterative delivery plan. Expect to work with common stacks for ML ops (Python, Docker, Kubernetes, PostgreSQL or similar) and to weave in explainability libraries such as SHAP or LIME where useful. A lightweight UI in React or Vue is welcome if it speeds adoption, but a clean API façade that can plug into existing dashboards is equally acceptable.
Acceptance criteria:
1. Technical design document approved by me before coding starts.
2. Minimum-viable product deployed to a cloud test environment with automated unit and integration tests.
3. Demo walkthrough showing a complete governance path—from data registration to final model sign-off—with audit export.
If you have released comparable governance, compliance, or risk-management tools before, highlight that experience so I can gauge fit quickly.