Question 1

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

Accepted Answer

Azure Machine Learning is for building and operating your own machine learning models (custom training, tuning, deployment, and MLOps). Azure AI Services are prebuilt APIs for common AI tasks like vision, speech, language, and document processing, where you typically don’t train a full custom model from scratch.

Question 2

When should I use Azure Machine Learning?

Accepted Answer

Use it when you need to train custom models, manage experiments, track datasets and model versions, automate ML pipelines, or deploy models as real-time endpoints or batch jobs. It’s a good fit when you want a repeatable MLOps workflow (dev/test/prod), need scalable GPU/CPU training, or must integrate with Azure security, networking, and governance.

Question 3

How much does Azure Machine Learning cost?

Accepted Answer

Azure Machine Learning itself has workspace and feature considerations, but most cost typically comes from the underlying resources you use: compute instances for notebooks, compute clusters for training, GPU/CPU VM sizes and run time, storage for datasets and artifacts, container registry, and online endpoints (compute and scaling). Costs vary based on model size, training duration, concurrency, autoscaling settings, and whether you use GPUs. Use Azure Pricing Calculator and set budgets/alerts to control spend.

Azure Machine Learning

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