Design an ML Experiment Tracking System — System Design Interview Practice
Design an experiment tracking system that logs hyperparameters, metrics, artifacts, and code for ML experiments, enabling comparison, reproducibility, and team collaboration. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Log hyperparameters, metrics, and artifacts per runRequirement
- Track code version and environment for reproducibilityRequirement
- Compare experiments with interactive visualizationsRequirement
- Organize experiments into projects and groupsRequirement
- Support distributed and multi-node experiment loggingRequirement
- Search and filter experiments by metrics/parametersRequirement
- Collaborate with team annotations and commentsRequirement
- Integrate with popular ML frameworks (PyTorch, TF, sklearn)Requirement
- mlopsConcept to explore
- experiment trackingConcept to explore
- reproducibilityConcept to explore
- hyperparametersConcept to explore
- metricsConcept to explore
- collaborationConcept to explore