Data Science

Announcing preview of Azure Data Share

Donnerstag, 11. Juli 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

Montag, 10. Juni 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

Donnerstag, 9. Mai 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

Unter den neuesten Innovationen unübertroffen: Analysen in Azure

Montag, 6. Mai 2019

Der digitale Wandel birgt für Unternehmen ein unglaubliches Potenzial, um Daten im Wettbewerb zu Ihrem Vorteil einzusetzen. Deshalb sind Analysen für Unternehmen weiterhin von höchster Priorität. In Bezug auf Analysen hören wir immer wieder von Kunden, dass sie eine Lösung benötigen, die Sicherheit und Datenschutz genauso wie das bestmögliche Preis-Leistungs-Verhältnis gewährleistet. Zudem muss das System einfach unkompliziert relevante Erkenntnisse für das gesamte Unternehmen liefern. Azure bietet all das.

Corporate Vice President, Azure Data

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

Montag, 29. April 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