model governance

A concise guide to AI model management across the ML lifecycle: organizing and versioning data, tracking experiments, training and tuning, registering models, automating CI/CD, deploying safely, and monitoring for drift, bias, and performance. It stresses governance, documentation, and responsible AI, using MLOps practices and cross‑functional collaboration to keep models reliable, scalable, and compliant.

An overview of practical AI security checklists across the ML lifecycle: governance and risk assessment, data sourcing and privacy, model training and hardening, supply-chain security, deployment and access controls, monitoring and incident response, and compliance. Provides actionable controls and prompts teams can adapt to build and operate safer, trustworthy AI.