MLOps vs DevOps comparison - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key difference between MLOps and DevOps

Which statement best describes a key difference between MLOps and DevOps?

A DevOps is only about monitoring, while MLOps is only about coding.

B DevOps requires managing data versioning and model retraining, but MLOps does not.

C MLOps focuses on managing machine learning models and data pipelines, while DevOps focuses on software application deployment and infrastructure automation.

D MLOps only deals with hardware provisioning, whereas DevOps handles software testing.

💡 Hint

Think about what each practice manages beyond just code.

MLOps extends DevOps by including data and model lifecycle management, not just software deployment.

🔀 Workflow

intermediate

MLOps pipeline step not in DevOps

Which step is unique to an MLOps pipeline compared to a typical DevOps pipeline?

A Continuous integration of application code

B Automated unit testing of software

C Infrastructure as code deployment

D Automated model training and validation

💡 Hint

Consider what extra step is needed for machine learning projects.

MLOps pipelines include automated model training and validation, which is not part of standard DevOps workflows.

❓ Troubleshoot

advanced

Troubleshooting model deployment failure in MLOps

You deployed a machine learning model using an MLOps pipeline, but predictions are incorrect. Which cause is most specific to MLOps compared to DevOps?

A Model drift due to changes in input data distribution

B Application server crashed due to memory leak

C Version control conflict in source code repository

D Incorrect environment variables set in deployment configuration

💡 Hint

Think about what can affect model accuracy after deployment.

Model drift, where input data changes over time, is a unique challenge in MLOps affecting prediction quality.

✅ Best Practice

advanced

Best practice for versioning in MLOps vs DevOps

Which versioning practice is recommended specifically for MLOps but not typically required in DevOps?

A Version control for infrastructure as code scripts

B Versioning datasets and trained models alongside code

C Tagging software releases in Git

D Using semantic versioning for application binaries

💡 Hint

Consider what additional artifacts need tracking in machine learning projects.

MLOps requires versioning datasets and models to ensure reproducibility, which is not common in traditional DevOps.

💻 Command Output

expert

Output of MLOps model deployment status command

Given the command mlops deploy status model123 outputs the following JSON, what is the deployment status of model123?

MLOps

{
  "model_id": "model123",
  "status": "deployed",
  "version": "v2.1",
  "last_updated": "2024-05-10T14:30:00Z"
}

A The model is currently deployed and active.

B The model deployment failed and is inactive.

C The model is in training and not deployed yet.

D The model version is outdated and deprecated.

💡 Hint

Look at the status field in the JSON output.

The status field shows "deployed", meaning the model is active and running.