ML Experiment Tracker

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Sets up and manages machine learning experiment tracking, hyperparameter optimization, and model registry workflows. Integrates with MLflow, Weights & Biases, and Optuna for systematic experimentation. Handles metric logging, artifact storage, model versioning, reproducibility, and automated hyperparameter search with early stopping.

mlops experiment-tracking mlflow wandb optuna hyperparameter-tuning model-registry reproducibility

Use Cases

Setting up MLflow tracking for team ML projects
Automated hyperparameter search with Optuna
Comparing model performance across experiments
Building reproducible training pipelines with config management
Model versioning and promotion workflows (staging → production)
Generating experiment reports with metrics and visualizations

Example Prompt

Set up a complete ML experiment tracking workflow for a text classification project.

Requirements:
1. MLflow tracking server configuration (local or remote)
2. Experiment structure with proper naming and tagging
3. Optuna hyperparameter optimization with:
   - Learning rate, batch size, model architecture search
   - Pruning of unpromising trials (MedianPruner)
   - 50 trials with TPE sampler
4. Automatic logging of:
   - Training/validation metrics per epoch
   - Confusion matrix and classification report
   - Model artifacts and config files
   - Git commit hash and environment info
5. Model registry with stage transitions
6. Comparison dashboard code

Generate complete Python code using MLflow + Optuna + PyTorch Lightning.

Recommended Models

claude-opus-4-6 gpt-5 gemini-2-5-pro

Compatible Tools

claude-codecursorkiroany

Modalities

Input: text, code

→

Output: text, code

Related Skills

data-analysis

Author

OpenModels Community

@openmodelsrun