On Thursday, August 8, 2019, GitHub announced the preview of GitHub Actions with support for Continuous Integration and Continuous Delivery (CI/CD). Actions makes it possible to create simple, yet powerful pipelines and automate software compilation and delivery. Today, we are announcing the preview of GitHub Actions for Azure.
MATCH_RECOGNIZE in Azure Stream Analytics significantly reduces the complexity and cost associated with building, modifying, and maintaining queries that match sequence of events for alerts or further data computation.
If you’re experiencing problems with your applications, a great place to start investigating solutions is through your Azure Service Health dashboard. In this blog post, we’ll explore the difference.
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.
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.
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.
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.
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.
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.
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.