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🧪 Experiment Tracking with MLflow

Log hyperparameter sweeps as 3D runs on an MLflow-style tracking grid and compare it against a chaotic pile of hand-named model files.

AI & Machine Learning3DAdvanced60 FPS

Log hyperparameter sweeps as tracked runs on a 3D grid, and see how that compares to a pile of hand-named, hard-to-trace model files.

🔬 What It Demonstrates

Each logged run is placed by its learning rate and tree depth, with height and colour encoding accuracy — mirroring how MLflow records parameters and metrics for every training run automatically.

🎮 How to Use

Pick a learning rate and tree depth, then log the run to grow a bar on the tracking grid. Switch the colour metric, watch the gold beam track the best run, and compare it to the untracked file pile.

💡 Did You Know?

MLflow's tracking store can hold thousands of runs per experiment — each queryable by parameter or metric — replacing folders full of ambiguously named files like model_v2_final_FINAL.pkl.

⚙ Under the hood

Log hyperparameter sweeps as 3D runs on an MLflow-style tracking grid and compare it against a chaotic pile of hand-named model files.

machine learninghyperparameter tuningmodel trackingmlflowdata scienceexperiment designThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install