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.