Question 1

What's the difference between Azure Machine Learning and Azure AI Services?

Accepted Answer

Azure Machine Learning is for building, training, and deploying your own machine learning models (including custom pipelines and MLOps). Azure AI Services (like Vision, Speech, and Language) are prebuilt APIs you call to add AI capabilities without training your own model. Use Azure Machine Learning when you need custom models, custom data, or full control over training and deployment.

Question 2

When should I use Azure Machine Learning?

Accepted Answer

Use Azure Machine Learning when you need to: train models on your own data; track experiments and compare runs; automate training and deployment with MLOps; deploy models as real-time endpoints or batch jobs; or manage models centrally with a registry. It’s a good fit for teams moving from notebooks to production, or organizations that need governance, repeatability, and secure deployment in Azure.

Question 3

How much does Azure Machine Learning cost?

Accepted Answer

Azure Machine Learning has no single flat price; you pay mainly for the underlying resources you use. Key cost drivers include: compute for training (CPU/GPU clusters, training duration), compute for inference (managed online endpoints or Kubernetes), storage for datasets and artifacts, networking (data egress), and any attached services (like Azure Container Registry, Key Vault, or monitoring). Costs vary widely based on model size, GPU needs, and how often you retrain or serve predictions.

Azure Machine Learning

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