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Found 20 Skills
Use the Statsig MCP to inspect and manage Statsig entities such as gates, experiments, dynamic configs, segments, metrics, audit logs, and results.
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML workflows.
MLflow experiment tracking via Python API. TRIGGERS - MLflow metrics, log backtest, experiment tracking, search runs.
Comprehensive MLOps workflows for the complete ML lifecycle - experiment tracking, model registry, deployment patterns, monitoring, A/B testing, and production best practices with MLflow
MLflow, model versioning, experiment tracking, model registry, and production ML systems
Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.
Create, modify, run, inspect, analyze, and report Python experiments that use liblaf.cherries. Use when Codex needs to work under exp/YYYY/mm/dd/group-name/, write or edit numbered scripts in src/, run them with CHERRIES_NAME and CHERRIES_TAGS, inspect Cherries/Comet logs and generated assets, or write Markdown reports in docs/.