Why Model approval workflows in MLOps? - Purpose & Use Cases

The Big Idea

What if your model deployment could go from chaotic to seamless with just one simple workflow?

The Scenario

Imagine a team manually reviewing machine learning models by emailing files back and forth, tracking feedback in spreadsheets, and waiting days for approvals before deployment.

The Problem

This manual process is slow, confusing, and full of mistakes. Important feedback gets lost, versions get mixed up, and delays cause frustration and lost opportunities.

The Solution

Model approval workflows automate review steps, track versions clearly, and notify stakeholders instantly, making the approval process smooth, fast, and reliable.

Before vs After

✗ Before

Email model.pkl to reviewer
Wait for feedback
Manually update status in spreadsheet

✓ After

Submit model to approval system
Reviewer approves or requests changes
Status updates automatically and triggers deployment

What It Enables

It enables fast, transparent, and error-free model deployment that everyone trusts.

Real Life Example

A data science team uses an approval workflow to quickly get their new fraud detection model reviewed and deployed, reducing deployment time from weeks to hours.

Key Takeaways

Manual model reviews are slow and error-prone.

Approval workflows automate and track every step clearly.

This leads to faster, safer model deployments.

Practice

1. What is the main purpose of a model approval workflow in MLOps?

easy

A. To delete old models from storage

B. To train models faster using more data

C. To deploy models automatically without any checks

D. To check and approve machine learning models before they are used in production

Solution

1. Step 1: Understand the role of model approval workflows

   Model approval workflows are designed to ensure models are reviewed and tested before use.

2. Step 2: Identify the correct purpose

   The main goal is to check and approve models to keep production safe and reliable.

3. Final Answer:

   To check and approve machine learning models before they are used in production -> Option D

4. Quick Check:

   Model approval = safety check before use [OK]

Hint: Approval means checking before use, not training or deleting [OK]

Common Mistakes:

• Confusing approval with training or deployment
• Thinking approval deletes models
• Assuming approval skips checks

2. Which of the following is the correct syntax to add a manual approval step in a typical MLOps pipeline YAML?

easy

A. steps:
- approval_step:
users: ['team_lead']

B. steps:
- name: manual_approval
type: approval
inputs:
approvers: ['team_lead']

C. steps:
- manual_approval:
approvers: 'team_lead'

D. steps:
- name: approval
type: manual
approvers: team_lead

Solution

1. Step 1: Review typical YAML structure for approval steps

   Approval steps usually have a name, type, and inputs including approvers as a list.

2. Step 2: Match syntax with correct YAML format

   steps: - name: manual_approval type: approval inputs: approvers: ['team_lead'] correctly uses 'name', 'type', and 'inputs' with a list of approvers.

3. Final Answer:

   steps: - name: manual_approval type: approval inputs: approvers: ['team_lead'] -> Option B

4. Quick Check:

   Correct YAML keys and list format = steps: - name: manual_approval type: approval inputs: approvers: ['team_lead'] [OK]

Hint: Look for 'name', 'type', and list of approvers in YAML [OK]

Common Mistakes:

• Using wrong keys like 'users' instead of 'approvers'
• Not using list format for approvers
• Incorrect indentation or missing 'type'

3. Given this simplified MLOps pipeline snippet, what will be the output status after running the approval step if the approver rejects the model?

steps:
  - name: train_model
    type: training
  - name: approve_model
    type: approval
    inputs:
      approvers: ['alice']
    on_reject: fail_pipeline

medium

A. Pipeline skips approval and deploys

B. Pipeline continues to deploy the model

C. Pipeline fails and stops immediately

D. Pipeline retries training automatically

Solution

1. Step 1: Understand the approval step behavior

   The approval step waits for 'alice' to approve or reject the model.

2. Step 2: Analyze the 'on_reject' action

   If rejected, the pipeline is set to 'fail_pipeline', which stops the process immediately.

3. Final Answer:

   Pipeline fails and stops immediately -> Option C

4. Quick Check:

   Reject triggers fail_pipeline = stop [OK]

Hint: 'on_reject: fail_pipeline' means stop on rejection [OK]

Common Mistakes:

• Assuming pipeline continues after rejection
• Thinking approval is skipped
• Believing training retries automatically

4. You have this approval step configuration but the pipeline never pauses for approval. What is the likely error?

steps:
  - name: approve_model
    type: approval
    inputs:
      approvers: 'bob'

medium

A. Approvers should be a list, not a string

B. Missing 'type' field for approval step

C. Approval step name must be 'manual_approval'

D. Pipeline needs a trigger to pause

Solution

1. Step 1: Check the 'approvers' format

   The 'approvers' field must be a list, e.g., ['bob'], not a plain string.

2. Step 2: Understand impact of wrong format

   Using a string causes the pipeline to ignore the approval step or not pause.

3. Final Answer:

   Approvers should be a list, not a string -> Option A

4. Quick Check:

   Approvers list format required = Approvers should be a list, not a string [OK]

Hint: Approvers must be a list, even if one person [OK]

Common Mistakes:

• Using string instead of list for approvers
• Changing step name unnecessarily
• Assuming pipeline triggers control pause

5. You want to create a model approval workflow that automatically approves models with accuracy above 90%, but requires manual approval otherwise. Which workflow design achieves this?

hard

A. Add an automatic test step checking accuracy, then a conditional approval step that auto-approves if accuracy > 90%, else manual approval

B. Only use manual approval for all models regardless of accuracy

C. Skip approval if accuracy is above 90%, else reject the model automatically

D. Deploy all models first, then ask for approval later

Solution

1. Step 1: Define automatic test for accuracy

   Use a test step to check if model accuracy is above 90% automatically.

2. Step 2: Set conditional approval based on test result

   If accuracy > 90%, auto-approve; otherwise, require manual approval to ensure safety.

3. Final Answer:

   Add an automatic test step checking accuracy, then a conditional approval step that auto-approves if accuracy > 90%, else manual approval -> Option A

4. Quick Check:

   Auto test + conditional approval = Add an automatic test step checking accuracy, then a conditional approval step that auto-approves if accuracy > 90%, else manual approval [OK]

Hint: Combine auto test with conditional manual approval [OK]

Common Mistakes:

• Skipping manual approval for low accuracy models
• Approving all models without checks
• Approving after deployment instead of before