ER Diagram template

Machine Learning Workflow ER Diagram Template

A ready-to-use ER diagram template mapping ML pipeline entities—data prep, training, evaluation, and deployment—ideal for data scientists and ML engineers.

An ER diagram for a machine learning workflow captures the core entities involved in building and shipping a model, along with the relationships that connect them. Typical entities include Dataset, PreprocessingJob, Model, TrainingRun, EvaluationMetric, and DeploymentEndpoint. Each entity holds attributes that matter to the pipeline—for example, a TrainingRun might store hyperparameters, start time, and status, while an EvaluationMetric records accuracy, F1 score, and the model version it belongs to. By laying these out in a structured diagram, teams gain a shared vocabulary and a blueprint for the database or metadata store that will track every stage of the ML lifecycle.

## When to Use This Template

Reach for this template when you are designing the metadata layer of an ML platform, onboarding new team members to an existing pipeline, or documenting model governance requirements for compliance reviews. It is especially useful before writing any ORM code or migration scripts, because spotting a missing foreign key—say, forgetting to link EvaluationMetric back to a specific TrainingRun—is far cheaper on a whiteboard than in production. Product managers and ML engineers can also use the diagram together during sprint planning to agree on what data needs to be persisted versus what can be recomputed on demand.

## Common Mistakes to Avoid

One frequent error is collapsing the entire pipeline into a single "Experiment" entity and stuffing every attribute into it. This creates a wide, flat table that is hard to query and impossible to extend when you add new pipeline stages like feature stores or A/B testing. Instead, keep each stage as its own entity and use explicit relationships. A second mistake is omitting cardinality notation: a Dataset can feed many TrainingRuns, and a TrainingRun can produce many EvaluationMetrics—leaving these as unlabeled lines hides critical business rules. Finally, teams often forget to model the DeploymentEndpoint-to-Model relationship carefully; a single endpoint may serve multiple model versions during a canary rollout, so that many-to-many relationship deserves its own associative entity with attributes like traffic_percentage and promoted_at timestamp.

View Machine Learning Workflow as another diagram type

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FAQ

What entities should I include in a machine learning workflow ER diagram?
At minimum, include Dataset, PreprocessingJob, Model, TrainingRun, EvaluationMetric, and DeploymentEndpoint. Add Feature and Experiment entities if your pipeline uses a feature store or tracks A/B tests.
How is an ER diagram different from a flowchart for an ML pipeline?
A flowchart shows the sequence of steps, while an ER diagram shows the data entities, their attributes, and the relationships between them. Use both together—the flowchart for process, the ER diagram for data structure.
Can I use this ER diagram template for MLOps database design?
Yes. The template maps directly to the tables or collections you will need in an MLOps metadata store. Each entity becomes a table, attributes become columns, and relationship lines guide your foreign key and join-table decisions.
How do I represent model versioning in an ML workflow ER diagram?
Add a version attribute to the Model entity and create a one-to-many relationship from Model to TrainingRun. For deployment, use an associative entity between DeploymentEndpoint and Model to store version-specific metadata like traffic split and deployment timestamp.