MLflow Model Registry in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the MLflow Model Registry?

The MLflow Model Registry is a centralized store to manage the lifecycle of machine learning models. It helps track model versions, stages, and annotations in one place.

beginner

Name the four main stages a model can have in the MLflow Model Registry.

The four main stages are: Staging, Production, Archived, and None (no stage assigned).

intermediate

How do you register a model in MLflow Model Registry using the CLI?

Use the command:

mlflow models register -m <model_uri> -n <model_name>

This registers a new model version under the given name.

beginner

What is the purpose of model versioning in MLflow Model Registry?

Model versioning keeps track of different iterations of a model. It helps teams compare, deploy, and roll back models safely.

intermediate

Explain how MLflow Model Registry supports collaboration in a team.

It provides a shared place where team members can see model versions, add comments, approve stages, and manage deployment status together.

Which MLflow Model Registry stage indicates a model is ready for production use?

A Production

B None

C Archived

D Staging

The 'Production' stage means the model is approved and ready for live use.

What command registers a model version in MLflow?

A mlflow models serve

B mlflow run

C mlflow models register

D mlflow experiments create

The 'mlflow models register' command registers a new model version.

What does the 'Archived' stage mean in MLflow Model Registry?

A Model is deprecated and not used

B Model is ready for testing

C Model is newly created

D Model is in production

Archived means the model is no longer active or recommended for use.

Which feature helps track different iterations of a model in MLflow?

A Model Training

B Model Staging

C Model Serving

D Model Versioning

Model versioning tracks different saved versions of a model.

How does MLflow Model Registry help teams collaborate?

A By sharing model code only

B By providing a shared place to manage model versions and stages

C By automating model training

D By deploying models automatically

It centralizes model info so teams can review, comment, and approve models together.

Describe the lifecycle stages of a model in MLflow Model Registry and their meanings.

Think about where a model is tested, used live, or retired.

Explain how MLflow Model Registry supports safe deployment and rollback of machine learning models.

Consider how keeping versions and stages helps manage changes.