Data Science

Announcing preview of Azure Data Share

jueves, 11 de julio de 2019

Collaborating on data across organizations and integrating it into business decision making is foundational to digital transformation initiatives in organizations. To enable rich data collaboration, a new capability is needed to make sharing data of any size and shape, simple and governed.

Director of Program Management, Information Management & Governance

Three things to know about Azure Machine Learning Notebook VM

lunes, 10 de junio de 2019

Data scientists have a dynamic role. They need environments that are fast and flexible while upholding their organization’s security and compliance policies. Notebook Virtual Machine (VM), announced in May 2019, resolves these conflicting requirements while simplifying the overall experience for data scientists.

Principal Program Manager, Azure Machine Learning

Take your machine learning models to production with new MLOps capabilities

jueves, 9 de mayo de 2019

At Microsoft Build 2019 we announced MLOps capabilities in Azure Machine Learning service. MLOps, also known as DevOps for machine learning, is the practice of collaboration and communication between data scientists and DevOps professionals to help manage the production of the machine learning (ML) lifecycle.

Senior Program Manager, Azure MLOps

Analytics en Azure se mantiene inigualable con algunas innovaciones

lunes, 6 de mayo de 2019

La interrupción digital ha abierto a las empresas posibilidades ilimitadas de adoptar datos como ventaja competitiva para sus negocios. Como resultado, los análisis siguen siendo una prioridad clave de las empresas. En lo que respecta a los análisis, los clientes nos cuentan que necesitan una solución que les ofrezca el mejor precio, rendimiento, seguridad y privacidad, además de un sistema que pueda ofrecer fácilmente conclusiones eficaces en la organización. Azure les ha proporcionado cobertura.

Corporate Vice President, Azure Data

Understanding HDInsight Spark jobs and data through visualizations in the Jupyter Notebook

lunes, 29 de abril de 2019

The Jupyter Notebook on HDInsight Spark clusters is useful when you need to quickly explore data sets, perform trend analysis, or try different machine learning models. Not being able to track the status of Spark jobs and intermediate data can make it difficult for data scientists to monitor and optimize what they are doing inside the Jupyter Notebook.

Senior Program Manager, Big Data Team