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MLflow Experiment Structure
A typical Chemistry experiment we did in school days.
| Experiment (ML Project) | Run # | Inputs (Parameters) | Process (Code/Recipe) | Outputs (Artifacts) | Metrics (Results) |
|---|---|---|---|---|---|
| Acid + Base Reaction | Run 1 | Acid=10ml, Base=5ml | Stirred 2 mins, room temp | Beaker with solution | pH=7.0 |
| Acid + Base Reaction | Run 2 | Acid=10ml, Base=7ml | Stirred 2 mins, room temp | Beaker with solution | pH=6.2 |
| Acid + Base Reaction | Run 3 | Acid=10ml, Base=7ml | Stirred 5 mins, heat | Beaker with solution | pH=6.0, Color=yellow |
- Experiment → Group of related trials (like a project or ML task).
- Run → One trial with a unique ID (just like a single lab experiment entry).
- Inputs (Parameters) → Model hyperparameters (learning rate, batch size, etc.).
- Process (Code/Recipe) → Training code or pipeline steps.
- Outputs (Artifacts) → Models, plots, datasets, or serialized files.
- Metrics (Results) → Accuracy, loss, F1-score, etc.
MLflow
│
├── Experiment A
│ ├── Run 1
│ │ ├── Parameters
│ │ ├── Metrics
│ │ ├── Artifacts
│ │ └── Tags
│ ├── Run 2
│ │ ├── Parameters
│ │ ├── Metrics
│ │ ├── Artifacts
│ │ └── Tags
│ └── Run 3
│ ├── Parameters
│ ├── Metrics
│ ├── Artifacts
│ └── Tags
│
└── Experiment B
├── Run 1
├── Run 2
└── Run N
git clone https://github.com/gchandra10/uni_multi_model.git