Databricks Machine Learning Associate Question 404
Select 3A data science team is building a machine learning model using Databricks and wants to implement an MLOps strategy. Which of the following best practices should they follow to ensure reproducibility, scalability, and deployment efficiency?
- A
Version control all code, data, and model artifacts using a system like Git or Databricks Repos.
- B
Manually track model hyperparameters and metrics in a local spreadsheet for future reference.
- C
Use automated CI/CD pipelines to test, validate, and deploy models to production environments.
- D
Deploy models directly to production without validation to reduce the time-to-market.
- E
Leverage feature stores to ensure consistency of feature computation across training and inference.
Show answer and explanation
Correct answers: A, C, E
Explanation
To implement a robust MLOps strategy, it is important to version control code and artifacts, use automated CI/CD pipelines for deployment, and leverage feature stores for feature consistency. These practices ensure reproducibility, scalability, and efficiency throughout the machine learning lifecycle. Avoiding manual processes and unvalidated deployments reduces risks and enhances reliability.
- A. Correct.
Version control is a critical practice in MLOps, ensuring that code, data, and model artifacts are reproducible and trackable. This is essential for collaboration and debugging.
- B. Incorrect.
Manually tracking model details in a spreadsheet is prone to errors and not scalable. Tools like MLflow should be used instead for tracking experiments and model metadata.
- C. Correct.
Automated CI/CD pipelines are a fundamental component of MLOps, enabling the seamless and reliable deployment of models while ensuring quality and validation checks.
- D. Incorrect.
Deploying models directly to production without validation is a risky practice that can lead to unreliable results and potential failures in production.
- E. Correct.
Feature stores are an essential tool in MLOps to ensure consistency in feature engineering between the training and inference stages, which is critical for model accuracy and performance.