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Azure Machine Learning studio web experience is now generally available and bringing new features.
To help organizations overcome barriers to adopting artificial intelligence (AI), Microsoft is announcing several new Responsible ML innovations in Azure Machine Learning.
Microsoft Azure Machine Learning empowers developers and data scientists with enterprise-grade capabilities to accelerate the ML lifecycle. At Microsoft Build 2020, we announced several advances to Azure Machine Learning across the following areas: ML for all skills, Enterprise grade MLOps, and responsible ML.
Log streaming is now available in the Azure Machine Learning studio UI.
Azure Machine Learning is now available in US Gov Virginia and US Gov Arizona.
Target availability: Q1 2020
Implement machine learning models as a user-defined function (UDF) in your Azure Stream Analytics jobs to do real-time scoring and predictions on your streaming input data.
Azure Machine Learning becomes a first party event publisher for Azure Event Grid, publishing events about model training run, model registration, model deployment and data drift detection
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.
ML.NET lets developers create models targeting scenarios based on ML tasks, such as classification, regression, clustering, ranking, recommendations and anomaly detection. It also provides integration with other deep-learning frameworks, such as TensorFlow, and it offers interoperability through ONNX. By using ML.NET, developers can accomplish those tasks without the need for high expertise in data science or machine learning. Learn more
New updates to Azure Machine Learning simplify the building, training and deployment of machine learning models for enterprises. These include new capabilities to simplify the development of machine learning models, operationalize models at scale and high-speed inferencing from cloud to edge. Azure Machine Learning helps data scientists and developers build and train AI models faster, then easily deploy those models to the cloud or the edge. Read blog
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.
Azure at Build
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