Model approval workflows in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a model approval workflow in MLOps?

A model approval workflow is a process that ensures machine learning models meet quality and compliance standards before deployment. It involves steps like testing, validation, and review by stakeholders.

beginner

Why is model approval important before deployment?

Model approval helps prevent deploying models that could cause errors, bias, or poor performance. It ensures models are safe, reliable, and meet business goals.

intermediate

Name a common step in a model approval workflow.

Common steps include automated testing, performance evaluation, fairness checks, and manual review by data scientists or business owners.

intermediate

How can automation help in model approval workflows?

Automation speeds up testing and validation, reduces human errors, and ensures consistent checks before approval, making the workflow efficient and reliable.

beginner

What role do stakeholders play in model approval workflows?

Stakeholders review model results, check compliance with policies, and give final approval to ensure the model aligns with business needs and ethical standards.

What is the main goal of a model approval workflow?

A To collect more data for training

B To speed up model training

C To ensure models are tested and safe before deployment

D To deploy models without review

The main goal is to ensure models are tested and safe before deployment.

Which step is NOT typically part of a model approval workflow?

A Performance evaluation

B Fairness checks

C Manual review

D Random data deletion

Random data deletion is not part of model approval workflows.

Who usually gives the final approval in a model approval workflow?

A Data scientists or business stakeholders

B Anyone on the internet

C Only the machine learning model

D Automated scripts without human input

Final approval is given by data scientists or business stakeholders.

How does automation improve model approval workflows?

A By making testing faster and consistent

B By removing all human checks

C By ignoring model performance

D By skipping validation steps

Automation makes testing faster and consistent, improving workflow efficiency.

What risk does model approval help reduce?

A Increasing training data size

B Deploying biased or poor-performing models

C Faster model training

D Ignoring business goals

Model approval helps reduce the risk of deploying biased or poor-performing models.

Explain the key steps involved in a model approval workflow and why each is important.

Think about how each step helps catch problems before deployment.

Describe how automation and human review work together in model approval workflows.

Consider the strengths of machines and humans in the process.

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