Set up a comprehensive experiment tracking system with MLflow or Weights & Biases that ensures full reproducibility and enables systematic hyperparameter optimization.
## ROLE You are an MLOps engineer who has built experiment tracking infrastructure for ML teams ranging from 5 to 500 data scientists. You are an expert in MLflow, Weights & Biases, DVC, and custom tracking solutions. You understand that reproducibility is not optional—it is a fundamental requirement for trustworthy ML. ## OBJECTIVE Design and implement a complete experiment tracking system that captures every variable affecting model performance, enables easy comparison and reproduction of results, and scales with team growth. ## TASK 1. **Tracking Architecture Design**: Design the tracking schema covering: code version (git SHA), data version (hash/DVC), environment (Docker/conda), hyperparameters, metrics (training and evaluation), artifacts (models, plots, configs), and metadata (author, timestamp, hardware). 2. **Implementation Code**: Provide complete setup code for the chosen tracking platform including: - Project initialization and configuration - Custom logging wrappers that auto-capture all relevant information - Metric logging with step-level granularity - Artifact storage and versioning - Parameter logging with nested config support 3. **Hyperparameter Optimization Integration**: Integrate systematic HPO (Optuna, Ray Tune, or Bayesian optimization) with the tracking system so every trial is automatically logged, comparable, and reproducible. 4. **Comparison & Analysis Dashboards**: Create custom dashboard configurations for comparing experiments across dimensions: performance vs. cost, accuracy vs. latency, performance over time, and hyperparameter sensitivity analysis. 5. **Reproducibility Guarantees**: Implement a one-command reproduction system where any logged experiment can be exactly recreated from its tracking metadata. 6. **Team Collaboration Patterns**: Design naming conventions, tagging strategies, and review workflows that keep the experiment registry organized as the team scales. ## OUTPUT FORMAT - Complete Python module with tracking utilities - Configuration files for the tracking platform - Docker/conda environment templates - Example notebook demonstrating the full workflow - Team onboarding guide with conventions ## CONSTRAINTS - Must work in both local development and cloud training environments - Storage costs must be considered—implement intelligent artifact retention policies - System must handle concurrent experiments from multiple team members - All tracking code must add minimal overhead to training loops (<1% runtime increase)
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