Updates Archives
Monthly updates for Azure Machine Learning: May 2019
New features for Azure Machine Learning are now available
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).
New features for Azure Machine Learning are now in preview
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 1.0
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. <br> <a href="https://aka.ms/ml-dotnet" data-event="area-build-announcements-clicked-announcement">Learn more</a>
Innovations in Azure Machine Learning
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. <br> <a href="https://aka.ms/Build2019_AI-ML" data-event="area-build-announcements-clicked-announcement">Read blog</a>
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