ML model training pipeline template

Components in this data flow diagram

• Raw training data
• Train / validation / test split
• Preprocess & feature pipeline
• Hyperparameter search
• Train candidate models
• Experiment tracking
• Beats baseline on validation?
• Evaluate on held-out test
• Bias & fairness checks pass?
• Model registry: staging
• Shadow deploy
• A/B test wins?
• Promote to production
• Drift monitoring

How to use this data flow diagram template

1. Click Open in the editor. The data flow diagram opens on a new page in FlowDiagram — nothing is uploaded and there is no sign-up.
2. Rename, add or delete shapes and drag them where you want; connectors stay attached as things move.
3. Drop in vendor logos from the library — 1,160 of them, including every AWS, Azure and Google Cloud service icon — or restyle it with the colour and theme controls.
4. Prefer text? Edit the Mermaid source below and paste it in through ☰ → Import diagram (see the Mermaid editor); it imports as an editable diagram.
5. Export to PNG, SVG or PDF, or press Ctrl+L for a link that carries the whole diagram.

Mermaid source

The whole template is the text below. Paste it into FlowDiagram, keep it in Git next to the code it describes, or hand it to an AI assistant to extend.

flowchart LR
  DATA[(Raw training data)] --> SPLIT[Train / validation / test split]
  SPLIT --> PREP[Preprocess & feature pipeline]
  PREP --> HPO[Hyperparameter search]
  HPO --> TRAIN[Train candidate models]
  TRAIN --> TRACK[(Experiment tracking)]
  TRAIN --> EVAL{Beats baseline on validation?}
  EVAL -->|No| HPO
  EVAL -->|Yes| TEST[Evaluate on held-out test]
  TEST --> FAIR{Bias & fairness checks pass?}
  FAIR -->|No| DATA
  FAIR -->|Yes| REG[Model registry: staging]
  REG --> SHADOW[Shadow deploy]
  SHADOW --> AB{A/B test wins?}
  AB -->|No| REG
  AB -->|Yes| PROD[Promote to production]
  PROD --> MON[Drift monitoring]

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