mlflow-experiment-tracker

# MLflow Experiment Tracker Integrate with MLflow for comprehensive ML experiment tracking, model registry operations, and artifact management. ## Overview This skill provides capabilities for interacting with MLflow's tracking server and model registry. It enables automated experiment logging, run comparison, model versioning, and artifact retrieval within ML workflows. ## Capabilities ### Experiment Management - Create and manage experiments - Start and end runs programmatically - Set experiment tags and descriptions - List and search experiments ### Parameter and Metric Logging - Log hyperparameters for reproducibility - Track metrics during training (loss, accuracy, etc.) - Log batch metrics with timestamps - Set run tags for organization ### Artifact Management - Log model artifacts (serialized models, checkpoints) - Store datasets and data samples - Save plots and visualizations - Retrieve artifacts from completed runs ### Model Registry Operations - Register trained models - Manage model versions - Transition models between stages (Staging, Production, Archived) - Add model descriptions and tags ### Run Comparison and Analysis - Compare metrics across runs - Search runs by parameters/metrics - Retrieve best performing runs - Generate comparison visualizations ## Prerequisites ### MLflow Installation ```bash pip install mlflow>=2.0.0 ``` ### MLflow Tracking Server Configure tracking URI: ```python import mlflow mlflow.set_tracking_uri("http://localhost:5000"...