AI-900: What Azure Service is Used for Managing and Orchestrating Machine Learning Experiments?

Discover which Azure service – Azure Synapse Analytics, Data Lake, Machine Learning, or Cognitive Services – is primarily used for managing and orchestrating machine learning experiments. Prepare for the AI-900 Microsoft Azure AI Fundamentals certification exam with this comprehensive explanation.

Question

Which Azure service is mainly used for managing and orchestrating ML experiments?

A. Azure Synapse Analytics
B. Azure Data Lake
C. Azure Machine Learning
D. Azure Cognitive Services

Answer

Azure Machine Learning is the correct answer for the Azure service that is mainly used for managing and orchestrating machine learning experiments.

C. Azure Machine Learning

Explanation

Azure Machine Learning provides a cloud-based platform for building, training, and deploying machine learning models at scale. It offers a centralized place to work with all the artifacts for a machine learning project, including notebooks, environments, datasets, experiments, pipelines, models and endpoints.

With Azure Machine Learning, data scientists and ML engineers can:

• Create and manage ML workspaces to organize ML resources
• Author and run ML experiments to train and evaluate models
• Create and manage compute targets for model training and deployment
• Register, package, and deploy models to cloud and edge
• Track and monitor models in production
• Automate the ML lifecycle with pipelines

Some key features include:

• Drag-and-drop designer for no-code model development
• Automated machine learning (AutoML) to automatically try different algorithms and hyperparameters
• Tracking and versioning of models, data, and experiments
• DevOps capabilities for ML workflows
• Integration with open-source frameworks like PyTorch, TensorFlow, and scikit-learn

In contrast:

• Azure Synapse Analytics is an analytics service that brings together data integration, warehousing, and big data analytics. It’s used more for data engineering and business intelligence than ML experimentation.
• Azure Data Lake is a scalable data storage and analytics service for big data. While it can store data used for ML, it’s not a service for directly managing ML experiments.
• Azure Cognitive Services provides pre-built AI models for vision, speech, language, and decision-making. It offers APIs to incorporate AI capabilities into applications without building custom models. While useful for many AI scenarios, it’s not designed for custom ML experimentation.

So in summary, Azure Machine Learning is the go-to Azure service for professional data scientists building custom machine learning models and orchestrating end-to-end ML workflows. It provides the most comprehensive set of tools for the ML lifecycle compared to other Azure services.

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