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.
使用场景
- 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
示例提示词
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.
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模态
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相关 Skills
作者
OpenModels Community