2019年7月11日
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.
2019年7月3日
This year at Microsoft Build 2019, we announced a slew of new releases as part of Azure Machine Learning service which focused on MLOps. These capabilities help you automate and manage the end-to-end machine learning lifecycle.
2019年6月24日
With the proliferation of patient information from established and current sources, accompanied with scrupulous regulations, healthcare systems today are gradually shifting towards near real-time data integration.
2019年6月10日
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.
2019年6月4日
The automated machine learning capability in Azure Machine Learning service allows data scientists, analysts, and developers to build machine learning models with high scalability, efficiency, and productivity all while sustaining model quality.
2019年5月14日
In Craig Kerstiens‘s latest blog post, “A health check playbook for your Postgres database” he emphasizes the need for periodic checks for your Postgres databases to ensure it’s healthy and performing well.
2019年5月9日
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.
2019年5月6日
Digital disruption has created unlimited potential for companies to embrace data as a competitive advantage for their business. As a result, analytics continues to be a key priority for enterprises. When it comes to analytics, customers tell us that they need a solution that provides them with the best price, performance, security, and privacy, as well as a system that can easily deliver powerful insights across the organization. Azure has them covered.
2019年5月1日
Recommendation systems are used in a variety of industries, from retail to news and media. If you’ve ever used a streaming service or ecommerce site that has surfaced recommendations for you based on what you’ve previously watched or purchased, you’ve interacted with a recommendation system.
2019年4月29日
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.
