AI-900 Question 124
Select 4You are a data scientist tasked with building, training, and deploying machine learning models for your organization using Azure Machine Learning. Which capabilities of Azure Machine Learning would help you manage the entire machine learning lifecycle effectively?
- A
Experiment tracking and management to compare model performance over time
- B
Integrated support for deploying models to on-premises systems without additional tools
- C
Automated machine learning (AutoML) for building models without extensive coding
- D
Data labeling tools for preparing and annotating datasets
- E
Built-in support for managing Kubernetes clusters for large-scale deployments
Show answer and explanation
Correct answers: A, C, D, E
Explanation
Azure Machine Learning provides comprehensive tools and features to manage the entire machine learning lifecycle, including experiment tracking, AutoML, data labeling, and Kubernetes integration for deployment. These capabilities streamline the workflow for building, training, and deploying machine learning models, making it easier to manage at scale.
- A. Correct.
Experiment tracking and management is a core capability of Azure Machine Learning that allows data scientists to track and manage model performance, making it easier to compare results and iterate quickly.
- B. Incorrect.
Azure Machine Learning does not natively support deploying models directly to on-premises systems without additional tools or integrations. This is not a core capability of the service.
- C. Correct.
Automated machine learning (AutoML) is a powerful feature of Azure Machine Learning that simplifies model development, enabling users to build models with minimal coding and expertise.
- D. Correct.
Azure Machine Learning includes data labeling tools to help users annotate datasets, which is essential for training supervised machine learning models.
- E. Correct.
Azure Machine Learning provides built-in support for managing Kubernetes clusters, enabling users to deploy and scale models seamlessly for large-scale production environments.