MLflow setup and basics in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is MLflow?

MLflow is an open-source platform that helps manage the machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry.

beginner

Which command installs MLflow using pip?

Use pip install mlflow to install MLflow in your Python environment.

beginner

How do you start the MLflow tracking server locally?

Run mlflow ui in your terminal to start the MLflow tracking UI on your local machine at http://localhost:5000.

beginner

What is the purpose of MLflow Tracking?

MLflow Tracking records and queries experiments: it logs parameters, code versions, metrics, and output files to help compare different runs.

intermediate

Name the four main components of MLflow.

The four main components are: MLflow Tracking, MLflow Projects, MLflow Models, and MLflow Model Registry.

Which command starts the MLflow UI?

A mlflow start

B mlflow ui

C mlflow run

D mlflow server

The command mlflow ui launches the MLflow tracking user interface.

What does MLflow Tracking primarily help with?

A Writing machine learning code

B Deploying models to production

C Logging and comparing machine learning experiments

D Data cleaning

MLflow Tracking is designed to log parameters, metrics, and artifacts to compare experiments.

Which Python package do you install to use MLflow?

A pandas

B tensorflow

C scikit-learn

D mlflow

MLflow is installed via pip install mlflow.

Where does MLflow UI run by default after starting?

A http://localhost:5000

B http://localhost:8000

C http://127.0.0.1:8080

D http://0.0.0.0:3000

By default, MLflow UI runs on port 5000 at localhost.

Which MLflow component manages model versioning and lifecycle?

A Model Registry

B Projects

C Tracking

D Models

The Model Registry component manages model versions and lifecycle stages.

Explain how to set up MLflow tracking on your local machine.

Think about installation, starting the UI, and logging runs.

Describe the main components of MLflow and their roles.

Focus on the four core parts and what each does.