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What are the key components of an Azure Machine Learning workspace?
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Key components include: Compute (training clusters, inference endpoints), Datastores (connections to storage), Datasets (versioned data references), Experiments (training runs), Models (trained artifacts), Pipelines (workflow automation), and Endpoints (deployment targets for models).
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