eval-mlflow

What problem does it solve?

Bridges evaluation data flow by logging run results and syncing datasets with MLflow, enabling end-to-end tracking of experiments and feedback between the harness and MLflow traces.

Core Features & Use Cases

• MLflow experiment tracking: log parameters, metrics, and artifacts from evaluation runs to MLflow for consistent analytics.
• Dataset synchronization: push evaluation cases to MLflow datasets and maintain cross-run traceability.
• Feedback integration: attach judge and human feedback to traces and pull annotations back into the evaluation pipeline for optimization.
• Use case: a data science team validates model performance across many runs and compares results in a single MLflow dashboard.

Quick Start

Install the skill and configure MLflow tracking, then run the evaluation workflow to start logging results and syncing data.