Databricks Machine Learning Professional Question 61
Select 3You are tasked with deploying a machine learning model created by a team in your organization. The team mentioned that they logged the model using the 'sklearn' flavor in MLflow. What are the benefits of using MLflow flavors in this scenario?
- A
MLflow flavors standardize the model format, making it easier to use the model across different tools and libraries.
- B
MLflow flavors automatically tune hyperparameters during model training.
- C
MLflow flavors support deployment across multiple platforms, such as REST APIs, batch inference, and streaming.
- D
MLflow flavors ensure compatibility with specific machine learning libraries, such as scikit-learn or TensorFlow.
- E
MLflow flavors provide built-in monitoring for model performance in production.
Show answer and explanation
Correct answers: A, C, D
Explanation
MLflow flavors define a standard format for models, ensuring they can be easily used across a variety of tools, environments, and deployment platforms. They also specify compatibility with particular machine learning libraries (e.g., scikit-learn or TensorFlow), simplifying the deployment process. However, MLflow flavors do not handle hyperparameter tuning or provide built-in monitoring capabilities. These tasks require separate processes or tools.
- A. Correct.
Correct: MLflow flavors provide a standardized way to represent models, enabling seamless use across various tools and environments.
- B. Incorrect.
Incorrect: MLflow flavors do not handle hyperparameter tuning; this is a separate task during model training.
- C. Correct.
Correct: MLflow flavors support deployment to multiple platforms, ensuring flexibility in how models are used.
- D. Correct.
Correct: MLflow flavors define compatibility with specific machine learning libraries, such as scikit-learn, TensorFlow, and PyTorch.
- E. Incorrect.
Incorrect: MLflow flavors do not provide built-in monitoring for model performance; this would require additional tools or integrations.