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🧠 AI & Machine Learning · Database ERD

ML Experiment Tracking Schema

Tables that keep machine learning work reproducible: datasets and their versions, experiments, runs, metrics, and registered models with their deployments.

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Drawing diagram…

What this diagram shows

  • Every run records the exact dataset version it used
  • Metrics are stored per run for comparison
  • Deployments point to a registered model version

Prompt used

Database for experiment tracking: projects have experiments; an experiment has runs; each run uses one dataset version, stores parameters and metrics; a run can produce a model version in the registry; model versions have deployments to environments.

Mermaid code
erDiagram
  PROJECT ||--o{ EXPERIMENT : has
  EXPERIMENT ||--o{ RUN : contains
  DATASET ||--|{ DATASET_VERSION : "versioned as"
  DATASET_VERSION ||--o{ RUN : "used by"
  RUN ||--o{ METRIC : logs
  RUN ||--o{ PARAM : uses
  RUN ||--o| MODEL_VERSION : produces
  MODEL_VERSION ||--o{ DEPLOYMENT : "deployed as"
  PROJECT {
    int id PK
    string name
    string owner
  }
  EXPERIMENT {
    int id PK
    int project_id FK
    string goal
  }
  DATASET {
    int id PK
    string name
  }
  DATASET_VERSION {
    int id PK
    int dataset_id FK
    string hash
    date created_on
  }
  RUN {
    int id PK
    int experiment_id FK
    int dataset_version_id FK
    string status
    datetime started_at
  }
  METRIC {
    int id PK
    int run_id FK
    string name
    decimal value
  }
  PARAM {
    int id PK
    int run_id FK
    string name
    string value
  }
  MODEL_VERSION {
    int id PK
    int run_id FK
    string stage
  }
  DEPLOYMENT {
    int id PK
    int model_version_id FK
    string environment
    date deployed_on
  }

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