New features for Azure Machine Learning are now available Azure updates Microsoft Azure

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). The SDK creates a common API across the integrated libraries and integrates Azure Machine Learning services. Using this SDK, you can 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. Forecasting via AutomatedML, Automated ML advancements and AutomatedML supported on Databricks, CosmosDB & HDInsight – Automated ML automates parts of the ML workflow, reducing the time it takes to build ML models, freeing data scientists to focus on their important work, while simplifying ML and opening it up to a wider audience.