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03 July 2019
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.
19 June 2019
In October 2018 we announced the public preview of Azure Monitor for VMs. At that time, we included support for monitoring your virtual machine scale sets from the at scale view under Azure Monitor. Today we are announcing the public preview of monitoring your Windows and Linux virtual machine scale sets from within the scale set resource blade.
17 June 2019
Last month, Microsoft released Azure Blockchain Service making it easy for anyone to quickly setup and manage a blockchain network and providing a foundation for developers to build a new class of multi-party blockchain applications in the cloud.
30 May 2019
Azure Deployment Manager is a new set of features for Azure Resource Manager that greatly expands your deployment capabilities. If you have a complex service that needs to be deployed to several regions, if you’d like greater control over when your resources are deployed in relation to one another, or if you’d like to limit your customer’s exposure to bad updates by catching them while in progress, then Deployment Manager is for you.
23 May 2019
Logs are critical for many scenarios in the modern digital world. They are used in tandem with metrics for observability, monitoring, troubleshooting, usage and service level analytics, auditing, security, and much more. Any plan to build an application or IT environment should include a plan for logs.
23 May 2019
oday, I’m excited to share our ability to support US Federal Risk and Authorization Management Program (FedRAMP) High impact level FedRAMP services with the extension of FedRAMP High Provisional Authorization to Operate (P-ATO) to all of our Azure public regions in the United States.
09 May 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.
07 May 2019
Azure Functions constantly innovates so that you can achieve more with serverless applications, enabling developers to overcome common serverless challenges through a productive, event-driven programming model.
06 May 2019
With GitHub and Azure DevOps, Microsoft is helping developers plan, build and ship any app, from large enterprise solutions to open source projects.
03 May 2019
With the exponential rise of data, we are undergoing a technology transformation, as organizations realize the need for insights driven decisions. Artificial intelligence (AI) and machine learning (ML) technologies can help harness this data to drive real business outcomes across industries. Azure AI and Azure Machine Learning service are leading customers to the world of ubiquitous insights and enabling intelligent applications such as product recommendations in retail, load forecasting in energy production, image processing in healthcare to predictive maintenance in manufacturing and many more.
