Databricks Machine Learning Associate Question 411
Select 3A data science team is building a machine learning model on Databricks. They decide to use a Databricks ML runtime for their project. Which of the following are advantages of using ML runtimes in this scenario?
- A
Pre-installed libraries and frameworks for machine learning and deep learning workflows
- B
Automatic setup of a feature store for all datasets used in the workspace
- C
Built-in optimizations for distributed training and inference
- D
Access to pre-trained models specific to Databricks ML runtimes
- E
Seamless integration with popular ML tools like MLflow for experiment tracking
Show answer and explanation
Correct answers: A, C, E
Explanation
The Databricks ML runtimes are tailored to accelerate machine learning workflows by providing pre-installed libraries, optimizations for distributed tasks, and native integrations with tools like MLflow. These advantages streamline the development, training, and tracking of machine learning models while reducing the effort required for environment setup.
- A. Correct.
ML runtimes on Databricks include pre-installed libraries and frameworks, such as scikit-learn, TensorFlow, PyTorch, and XGBoost, reducing setup time for common ML workflows.
- B. Incorrect.
While the Databricks platform supports feature stores, ML runtimes do not automatically create or set up feature stores for datasets used in the workspace.
- C. Correct.
ML runtimes provide optimizations for distributed training and inference, leveraging the scalability of Databricks clusters for efficient model training and deployment.
- D. Incorrect.
Databricks ML runtimes do not come with pre-trained models; users need to bring their own pre-trained models or train models from scratch.
- E. Correct.
Databricks ML runtimes are designed to integrate seamlessly with ML tools like MLflow, allowing users to track experiments and manage the ML lifecycle more efficiently.