Automated model validation before promotion in MLOps - Step-by-Step Execution

Process Flow - Automated model validation before promotion

New Model Trained

↓

Run Automated Tests

↓

Validation Passed?

No→Reject Model

Yes↓

Promote Model to Production

The flow shows a new model being tested automatically, then either rejected or promoted based on test results.

Execution Sample

MLOps

def validate_model(model):
    accuracy = model.test_accuracy()
    if accuracy >= 0.9:
        return True
    else:
        return False

This code checks if the model's accuracy is at least 90% to decide promotion.

Process Table

Step	Action	Model Accuracy	Condition	Result
1	Model trained	0.92	N/A	Proceed to validation
2	Check accuracy >= 0.9	0.92	True	Validation passed
3	Promote model	0.92	N/A	Model promoted to production
4	Model trained	0.85	N/A	Proceed to validation
5	Check accuracy >= 0.9	0.85	False	Validation failed
6	Reject model	0.85	N/A	Model rejected, no promotion

💡 Execution stops after model is either promoted or rejected based on validation.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3 or 6
model_accuracy	N/A	0.92 or 0.85	Checked against 0.9	Final decision made

Key Moments - 2 Insights

Why does the model get rejected even if it was trained successfully?

Because the validation condition accuracy >= 0.9 failed as shown in step 5 of the execution table.

What happens if the model accuracy is exactly 0.9?

The condition accuracy >= 0.9 is True, so the model passes validation and is promoted, similar to step 2.

Visual Quiz - 3 Questions

Look at the execution table, what is the model accuracy at step 1 when the model passes validation?

A 0.92

B 0.85

C 0.9

D 0.88

💡 Hint

Check the 'Model Accuracy' column at step 1 where validation proceeds.

At which step does the validation condition fail for the model?

A Step 2

B Step 5

C Step 3

D Step 6

💡 Hint

Look at the 'Condition' column for 'False' value indicating failure.

If the accuracy threshold changed to 0.95, how would the promotion step change?

A More models would be promoted

B No change in promotion

C Fewer models would be promoted

D All models would be rejected

💡 Hint

Higher threshold means stricter condition, so fewer models pass validation.

Concept Snapshot

Automated model validation checks model quality before promotion.
Use accuracy or other metrics as criteria.
If model meets threshold, promote to production.
If not, reject and retrain.
This ensures only good models serve users.

Full Transcript

This visual execution shows how a new machine learning model is automatically validated before being promoted to production. First, the model is trained and tested to get its accuracy. Then, an automated check compares the accuracy to a threshold, for example 0.9. If the accuracy is equal or above 0.9, the model passes validation and is promoted to production. If the accuracy is below 0.9, the model fails validation and is rejected. The execution table traces two example models: one with 0.92 accuracy that passes and is promoted, and one with 0.85 accuracy that fails and is rejected. The variable tracker shows how the model accuracy changes from training to validation to final decision. Key moments clarify why a model can be rejected despite training and what happens at boundary accuracy values. The quiz tests understanding of accuracy values at steps, failure points, and effects of changing thresholds. This process helps keep production models reliable by automating quality checks before promotion.