Promoting models between stages in MLOps - Step-by-Step Execution

Process Flow - Promoting models between stages

Model Training Completed

↓

Register Model in Registry

↓

Approve Model for Staging

↓

Deploy Model to Staging Environment

↓

Test and Validate in Staging

↓

Approve Model for Production

↓

Deploy Model to Production

↓

Monitor Model Performance

↓

Repeat or Rollback if Needed

This flow shows how a trained model moves step-by-step from training to production, passing through registration, staging, approval, deployment, and monitoring.

Execution Sample

MLOps

register_model('model_v1')
approve_model('model_v1', 'staging')
deploy_model('model_v1', 'staging')
approve_model('model_v1', 'production')
deploy_model('model_v1', 'production')

This code registers a model, promotes it to staging, deploys it there, then promotes and deploys it to production.

Process Table

Step	Action	Model Version	Stage	Result
1	register_model	model_v1	None	Model registered in registry
2	approve_model	model_v1	staging	Model approved for staging
3	deploy_model	model_v1	staging	Model deployed to staging environment
4	approve_model	model_v1	production	Model approved for production
5	deploy_model	model_v1	production	Model deployed to production environment

💡 Model is successfully deployed to production after passing staging approval and deployment

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5
model_v1_stage	None	registered	staging_approved	staging_deployed	production_approved	production_deployed

Key Moments - 3 Insights

Why can't we deploy the model directly to production without staging?

Staging acts as a safe testing area to validate the model before production. As shown in execution_table rows 3 and 5, deployment to staging happens first to catch issues early.

What does 'approve_model' do in the promotion process?

'approve_model' changes the model's stage status, allowing deployment to that stage. See rows 2 and 4 where approval precedes deployment.

What happens if the model fails tests in staging?

The model is not approved for production and deployment stops. This is implied by the flow stopping before step 4 if validation fails.

Visual Quiz - 3 Questions

Look at the execution_table, what is the model stage after step 3?

A staging_deployed

B production_approved

C registered

D production_deployed

💡 Hint

Check the 'Stage' and 'Result' columns at step 3 in the execution_table.

At which step is the model approved for production?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look for 'approve_model' action with 'production' stage in the execution_table.

If the model fails validation in staging, which step would NOT occur?

A register_model

B deploy_model to production

C approve_model for staging

D deploy_model to staging

💡 Hint

Refer to the concept_flow and key_moments about stopping deployment if staging validation fails.

Concept Snapshot

Promoting models between stages:
1. Register model in registry
2. Approve model for staging
3. Deploy to staging for testing
4. Approve for production after validation
5. Deploy to production
Always test in staging before production to avoid issues.

Full Transcript

This visual execution shows how a machine learning model moves through stages from training to production. First, the model is registered in a model registry. Then it is approved for the staging environment, where it is deployed and tested. If tests pass, the model is approved for production and deployed there. Monitoring continues after deployment. This step-by-step promotion ensures safe and reliable model updates.