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Creating the “Set Tags to ML Model Workflow”

The following steps walk you through building the SetTagsToMLModelWorkflow from start to finish. Each screenshot corresponds to a specific action you take while creating the workflow.

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Step 1: Create a New Workflow

Begin by navigating to: Govern → Workflows → New Workflow This opens the initial workflow creation screen. Here you provide:

• Workflow Name – A unique identifier without spaces (e.g., SetTagsToMLModelWorkflow)
• Description – A short summary of the workflow purpose Example: “Set Tags to ML Model if the description is complete.”

Click Save and Next to continue.

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Step 2: Configure the Start Node

After creating the workflow, you must configure the Start node. This determines which assets the workflow applies to and when it runs. Configure the following:

• Data Asset: Select Mlmodel
• Data Asset Filter: (Optional) Add filters such as owner or domain
• Trigger Type: Choose Periodic Batch or Event Based
• Schedule Type: Choose On Demand or set a recurring schedule
• Batch Size: Number of ML models processed per run

Click Save.

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Step 3: Add a Condition Check

Next, add a Check Condition node to verify whether the ML model has a description. Fill in the details:

• Display Name – Check NON NULL Description
• Description – Check if Description is Set
• Rules To Check –
  • Field: Description
  • Operator: Is Set

Click Save to add the check.

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Step 4: Configure the TRUE Condition Path

If the description is present, the workflow should assign a Complete tag. Connect the TRUE branch of the condition check to a Set Action node. Set:

• Source – Check Condition
• Result – TRUE
• Destination – The “Set Complete Tag” action node

Click Save.

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TRUE Path Action: Set Complete Tag

Now configure the action that applies the MachineLearning.Complete tag. Provide:

• Display Name – Set Complete Tag
• Description – Explanation of the action
• Field Name – Tags
• Field Value – MachineLearning.Complete

Click Save.

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Step 5: Configure the FALSE Condition Path

If the description is missing, tag the ML model as Incomplete. Here, set:

• Result – FALSE
• Destination – The “Set InComplete Tag” action node

Click Save.

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FALSE Path Action: Set Incomplete Tag

Configure the action to apply the MachineLearning.InComplete tag. Set:

• Display Name – Set InComplete Tag
• Description – Explain why this tag is used
• Field Name – Tags
• Field Value – MachineLearning.InComplete

Click Save.

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Step 6: End the Workflow

Both the TRUE and FALSE action nodes should connect to an End node, marking workflow completion. This ensures the workflow automatically stops after tagging the model appropriately.

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Final Workflow Overview

Once all nodes and connections are in place, your workflow should look like this:

• TRUE → Apply Complete Tag
• FALSE → Apply Incomplete Tag

This workflow fully automates documentation completeness tagging for ML models.

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Running a Periodic Batch Workflow

To execute an on-demand workflow, click Run Now This immediately triggers the workflow based on its configuration.

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Best Practices

1. Use the Right Type of Trigger
   • Event-Based Entity Triggers are ideal when specific fields must be automatically updated in response to a change. Example: When any attribute of a Glossary Term is modified, its status should automatically update to IN REVIEW.
   • Periodic Batch Triggers are best suited for bulk updates across many entities, especially for classification or enrichment workflows. Example: Tables or Dashboards can be classified as Tier 1, Tier 2, or Tier 3 based on the completeness of their column descriptions.
2. Use a Single Event-Based Workflow per Data Asset
   • Configure only one event-based entity workflow for each data asset. Having multiple workflows attempting to update the same field (such as the status of a Glossary Term) can result in unpredictable behavior, as one workflow’s changes may override another’s.
3. Optimize Batch Size for Periodic Workflows
   • Tune the batch size based on the number of data assets to ensure optimal performance.
   • Avoid running periodic workflows across all entities without filtering. Instead, apply an inclusion filter to limit the result set and prevent performance degradation.
4. Use User Approval Tasks Only in Event-Based Workflows
   • User Approval Tasks should be used exclusively in event-driven workflows. Using them in periodic workflows would generate multiple approval tasks simultaneously, overloading system resources.
   • If an approval step is needed in a periodic batch workflow, ensure the workflow scope is limited to a small, controlled set of entities.

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Limitations

1. User Approval Tasks Are Limited to Assets with Reviewer Support
   • User Approval Tasks can only be used for data assets that support assigning reviewers. Reviewer support for additional asset types will be introduced in future releases.
2. Fallback Behavior for Entities Without Reviewers
   • For entities that do not have any reviewers configured, User Approval Tasks automatically follow the TRUE path as a graceful fallback. Example: A Metric without a reviewer will automatically pass the approval step.