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Features include: Model Interpretability - Machine learning interpretability allows data scientists to explain machine learning models globally on all data, or locally on a specific data point using the state-of-art technologies in an easy-to-use and scalable fashion. Machine Learning interpretability incorporates technologies developed by Microsoft and proven third-party libraries (for example, SHAP and LIME).
Features include: Open Datasets - Open Datasets are a collection of datasets from the public domain to accelerate the development of machine learning models built in Azure. Open Datasets integrates with Machine Leaning Studio or can be accessed from python notebooks in Azure Machine Learning Service.
Stream Analytics now empowers every developer to easily add anomaly detection capabilities by providing ready-to-use machine learning models right within the SQL language.
Azure Machine Learning service, a cloud service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models, is now generally available.
Azure Media Services’ AI-based media metadata extraction service is now generally available.