Wavelength

Customer churn prediction ML pipeline diagram

This is a worked example. Wavelength built the diagram below from the description on this page. Nobody drew it by hand.

What was said

Product usage events and billing data land in the Databricks lakehouse. A feature pipeline builds the customer features every night. The churn model scores every account. If an account's churn risk is above 70 percent, the account is pushed to Salesforce and the customer success manager gets a task. MLflow tracks each model version, and the data science team retrains the model every month.

The diagram

G n1 Product Usage & Billing Data n2 Databricks Lakehouse n1->n2 land in n3 Feature Pipeline n2->n3 feeds n4 Churn Model n3->n4 builds features n5 Churn Risk >70%? n4->n5 scores n8 MLflow n4->n8 tracks n6 Salesforce n5->n6 Risk >70% n7 Customer Success Manager n5->n7 Risk >70% n9 Data Science Team n9->n4 retrain
Hexagons are systems. Cylinders are data stores. Diamonds are decisions. Rounded orange boxes are people and teams. A dashed amber arrow is a link the AI inferred. Pink arrows are decision branches. Blue arrows move data.

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What is in this flow

  • Systems: Feature Pipeline, Churn Model, Salesforce and MLflow.
  • Data stores: Databricks Lakehouse.
  • People and teams: Customer Success Manager and Data Science Team.
  • Decisions: Churn Risk >70%?.
  • Size: 9 steps and 8 connections.

Mermaid code

Copy this into any Markdown file, GitHub README, or Mermaid Live to render the same flow.

flowchart TD
  n1(["Product Usage & Billing Data"])
  n2[("Databricks Lakehouse"])
  n3{{"Feature Pipeline"}}
  n4{{"Churn Model"}}
  n5{"Churn Risk >70%?"}
  n6{{"Salesforce"}}
  n7("Customer Success Manager")
  n8{{"MLflow"}}
  n9("Data Science Team")
  n1 -->|land in| n2
  n2 -->|feeds| n3
  n3 -->|builds features| n4
  n4 -->|scores| n5
  n5 -->|Risk >70%| n6
  n5 -->|Risk >70%| n7
  n4 -->|tracks| n8
  n9 -->|retrain| n4

Map your own process

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