ML Experiment Tracking

Purpose

Track machine learning experiments with full reproducibility: log parameters, metrics, artifacts, and environment. Use the model registry to version, compare, and promote models through staging. Experiment tracking is the foundation of disciplined ML development — it transforms ad-hoc model building into a systematic, comparable, and repeatable process.

Architecture/Decision Trees

Platform Selection Decision Tree

Is your team size 1-3 data scientists working locally?
  |-- YES --> MLflow (local mode, simple setup)
  |-- NO --> Do you need rich collaboration features with non-ML stakeholders?
        |-- YES --> W&B (rich UI, reports, dashboards)
        |-- NO --> Do you have strict data sovereignty requirements?
              |-- YES --> MLflow (self-hosted, full control)
              |-- NO --> Do you need structured metadata with nested runs?
                    |-- YES --> Neptune (structured logging, comparison)
                    |-- NO --> MLflow (open standard, broad ecosystem)