Answer-first summary for fast verification
Answer: Databricks provides built-in support for deploying machine learning models as REST APIs, making it easy to integrate with applications for real-time predictions. It also provides tools for managing the models and handling scalability and performance.
Databricks provides built-in support for deploying machine learning models as REST APIs, which can be easily integrated with applications for real-time predictions. It also provides tools for managing the models, such as updating the models, handling scalability, and monitoring performance. Databricks MLflow helps in managing the machine learning lifecycle, including model training, deployment, and monitoring. This makes Databricks a comprehensive platform for deploying and managing machine learning models in a production environment.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
No comments yet.
In a scenario where you are working on a machine learning project that requires the deployment of trained models for real-time predictions, explain how Databricks can help in deploying and managing the models.
A
Databricks can only be used for training machine learning models, and model deployment is not supported.
B
Databricks can be used for deploying machine learning models, but managing the models and handling real-time predictions requires additional tools and infrastructure.
C
Databricks provides built-in support for deploying machine learning models as REST APIs, making it easy to integrate with applications for real-time predictions. It also provides tools for managing the models and handling scalability and performance.
D
Databricks can be used for deploying machine learning models, but it does not provide any support for real-time predictions or managing the models.