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Found 18 Skills
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Comprehensive MLOps workflows for the complete ML lifecycle - experiment tracking, model registry, deployment patterns, monitoring, A/B testing, and production best practices with MLflow
Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.
Use the Statsig MCP to inspect and manage Statsig entities such as gates, experiments, dynamic configs, segments, metrics, audit logs, and results.
Use this skill when deploying ML models to production, setting up model monitoring, implementing A/B testing for models, or managing feature stores. Triggers on model deployment, model serving, ML pipelines, feature engineering, model versioning, data drift detection, model registry, experiment tracking, and any task requiring machine learning operations infrastructure.
MLflow, model versioning, experiment tracking, model registry, and production ML systems