Track and manage AI/ML model versions using MLflow: log hyperparameters and metrics from experiments, register models to a central registry, transition between Staging and Production stages, compare performance across versions, and generate model cards. Generate production-ready AI/ML task code complete with validation, error handling, performance metrics, artifacts, and documentation.
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Set up ML experiment tracking
ML experiment tracking with metrics logging and run comparison
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
ML/perf investigation skills: topic, plan, judge, run, sweep