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Found 34 Skills
Create and manage structured documentation — experiments, plans, findings, checkpoints, research, learnings. Config-driven, parallel-safe.
MLflow ML lifecycle management. Use for ML experiment tracking.
Wandb Experiment Logger - Auto-activating skill for ML Training. Triggers on: wandb experiment logger, wandb experiment logger Part of the ML Training skill category.
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
Expert-level machine learning, deep learning, model training, and MLOps
Hyperparameter Tuner - Auto-activating skill for ML Training. Triggers on: hyperparameter tuner, hyperparameter tuner Part of the ML Training skill category.
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.
W&B integration. Manage data, records, and automate workflows. Use when the user wants to interact with W&B data.
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.
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.
Comet ML integration. Manage data, records, and automate workflows. Use when the user wants to interact with Comet ML data.