Describe how models move from data to production and the AI draws every stage, including the feedback edges that most hand-drawn pipelines leave out.
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Describe how models move from notebook to production and the AI draws the whole loop — data, training, registry, deployment, monitoring, retraining
Create machine learning pipeline diagrams showing data flow, feature engineering, training, and model deployment
Design end-to-end ML pipelines from data preparation through training, evaluation, deployment, and monitoring
Common questions about mlops pipeline diagram generator
The pipeline diagram shows stages in order: what runs, what gates it, what triggers the next step. The architecture diagram shows the platform components those stages run on. This page draws the pipeline view; the MLOps architecture page draws the component view. Many teams need both and link them.
Yes, and it should: monitoring detecting drift and triggering retraining is the part that makes it MLOps rather than a one-shot training script. Feedback edges from production monitoring back to the training stage render as first-class arrows.
MLflow, SageMaker, Vertex AI, Azure ML, Kubeflow, DVC, Weights and Biases, Feast, Seldon, BentoML, Evidently, plus orchestrators like Airflow and Dagster. Mixed cloud and open-source stacks render with the right icons.
Yes. Connect the Datadef MCP server to Claude Code or Cursor and the agent can read your pipeline definitions (SageMaker Pipelines code, Kubeflow manifests, CI workflows) and draw the diagram, then update it after refactors.
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