Skill

mlops-platform-engineering

Provides ML platform engineering based on internal developer platform practice for ML and AI systems, covering MLOps maturity levels, Kubeflow pipelines, MLflow tracking and registry, Feast feature stores with point-in-time correctness, BentoML serving, drift detection with statistical tests, monitoring and explainability integration, LLM observability, token cost economics and tiered model routing. Use when building or operating ML platforms, productionizing models, or adding LLMOps to an existing platform.

Bundled with Coacus. Its trust score, security findings and other files are on the plugin page.

npx [email protected] add dandgabr/Coacus