Databricks Machine Learning Associate Question 410
Select 4A data science team is building a machine learning pipeline on Databricks. They are considering using ML runtimes for their project. Which of the following are advantages of using ML runtimes in Databricks?
- A
Pre-installed and optimized versions of popular machine learning libraries
- B
Automatic hyperparameter tuning for all types of machine learning models
- C
Built-in support for distributed training on large datasets
- D
Access to GPU-optimized libraries for deep learning tasks
- E
Seamless integration with Databricks AutoML
Show answer and explanation
Correct answers: A, C, D, E
Explanation
Databricks ML runtimes provide an optimized environment for machine learning by including pre-installed libraries, support for distributed training, GPU optimizations, and integration with key Databricks tools like AutoML. These advantages significantly streamline the development and deployment of machine learning models.
- A. Correct.
ML runtimes in Databricks come with pre-installed and optimized versions of popular ML libraries such as scikit-learn, TensorFlow, and PyTorch, reducing the need for manual library setup.
- B. Incorrect.
ML runtimes do not provide automatic hyperparameter tuning for all models. This feature requires additional tools or frameworks, such as Hyperopt or Databricks AutoML.
- C. Correct.
ML runtimes support distributed training, which is critical for handling large datasets efficiently in distributed computing environments.
- D. Correct.
Databricks ML runtimes include GPU-optimized libraries, making them suitable for high-performance deep learning tasks.
- E. Correct.
ML runtimes integrate seamlessly with Databricks AutoML, enabling easy experimentation and automation of model development.