We are thrilled to announce the public preview of low-priority virtual machines (VMs) on VM scale sets. Low-priority VMs allow users to run their workloads at a fraction of the price, enabling significant cost savings. This offering has been available through our Azure Batch service since May 2017, and because we have seen great customer success we are expanding it to VM scale sets. This is a great option for resilient, fault-tolerant applications as these VMs are allocated using our unutilized capacity and can, therefore, be evicted. Low-priority VMs are available through VM scale sets with up to an 80 percent discount.
What are low-priority VMs?
Low-priority VMs enable you to take advantage of our unutilized capacity. The amount of available unutilized capacity can vary based on size, region, time of day, and more. When deploying Low-priority VMs in VM scale sets, Azure will allocate the VMs if there is capacity available, but there are no SLA guarantees. At any point in time when Azure needs the capacity back, we will evict low-priority VMs. Therefore, the low-priority offering is great for flexible workloads, like large processing jobs, dev/test environments, demos, and proofs of concept.
Provisioning low-priority VMs
Low-priority VMs can
I’m proud to announce that our Azure Health Information Trust Alliance (HITRUST) Common Security Framework (CSF) Certification was not only renewed by HITRUST, but our certification scope has expanded from last year by more than 250 percent! The HITRUST CSF Certification is the most widely recognized security accreditation in the healthcare industry. The HITRUST CSF builds on Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health (HITECH) Act, by providing a framework for complex compliance requirements that include technical and process elements such as HIPAA, National Institute of Standards and Technology (NIST), The Information Services Office (ISO) and Control Objectives for Information and Related Technologies (COBIT) to ensure controls are in place to safeguard Protected Health Information (PHI).
Health customers can further leverage our HITRUST CSF Certification as part of their own certification process when they build on Azure. To accelerate adoption and utilization for customers managing health data, we also recently released the Azure Security and Compliance Blueprint – HIPAA/HITRUST Health Data and AI, which provides tools and guidance for building HIPAA/HITRUST solutions.
Our greatly expanded HITRUST CSF assessment is another indication of our commitment to safeguarding information and maintaining
Azure Storage metrics in Azure Monitor, which was previously in public preview, is now generally available.
Azure Monitor is the platform service that provides a single source of monitoring data for Azure resources. With Azure Monitor, you can visualize, query, route, archive, and take action on the metrics and logs coming from resources in Azure. You can work with the data using the Monitor portal blade, the Azure Monitor Software Development Kits (SDKs), and through several other methods. Azure Storage is one of the fundamental services in Azure, and now you can chart and query storage metrics alongside other metrics in one consolidated view. For more information on how Azure Storage metrics are defined, you can see the documentation.
The features built on top of metrics are available differently per cloud:
Azure Monitor SDK (REST, .Net, Java & CLI): Available in all clouds Metric chart: Available in Public Cloud, and coming soon in Sovereign Clouds Alert: Available in Public Cloud, and coming soon in Sovereign Clouds
Meanwhile, the previous metrics become classic and are still supported. The following screenshot shows what the transition experience is. The Alerts and Metrics work on new metrics, and Alerts (classic), Metrics (classic), Diagnostic settings
https://powerbi.microsoft.com/en-us/blog/the-art-and-science-of-action-driven-visual-analytics/Source: https://powerbi.microsoft.com/en-us/blog/the-art-and-science-of-action-driven-visual-analytics/ Last year I went to our CMO Chris Capossela’s talk called “What’s Great Data in Microsoft”. In this talk, he listed five of the most important characteristics of good data: self-describe, fresh, forward READ MORE
This post is authored by Daniel Grecoe, Senior Software Engineer at Microsoft.
Today many platforms are moving towards hosting artificial intelligence models in self-managed container services such as Kubernetes. At Microsoft this is a substantial change from Azure Machine Learning Studio which provided all the model management and operationalization services for the user automatically.
To meet this need of self-managed container services Microsoft has introduced the Azure Machine Learning Workbench tool and the Azure services Machine Learning Experimentation, Machine Learning Model Management, Azure Container Registry and Azure Container Service.
With these new tools and services data science teams now have the freedom of wider language and model selection when creating AI new services, coupled with the choice of the infrastructure it is operationalized on. This choice enables the team to appropriately size the container service to meet the business requirements set forth for the model being operationalized while controlling costs associated with the service.
My recent blog post on Scaling Azure Container Service Clusters discussed determining the required size of a Kubernetes cluster based on formulae. The formulae took into account service latency, requests per second, and the hardware it is being operationalized on. The blog notes that the formulae
As businesses around the world continue to adopt Azure, it’s our mission to ensure our customers can trust our cloud. Today in Redmond, we invited the world to see how our teams are innovating in the Azure Cloud Collaboration Center, a first-of-its-kind facility that combines innovation and scale to address operational issues and unexpected events in order to drive new levels of customer responsiveness, security and efficiency.
We’re using the Cloud Collaboration Center to take a proactive approach to delivering responsiveness for our customers, who count on our cloud services in 140 countries and on all inhabited continents. To meet the mission-critical requirements businesses trust, we need to be always looking ahead. Delivering innovation in Azure services means identifying new efficiencies and finding new ways to streamline connections with the intelligence we have at every level of the cloud.
The Cloud Collaboration Center space gives customers a snapshot of what is happening with their data 24/7 and enables real-time troubleshooting of any issue by multiple teams simultaneously from across the organization. It is a space that’s purpose-built for collaborative work, with a 1,600 square foot video wall that enables a comprehensive view
PyTorch 1.0 takes the modular, production-oriented capabilities from Caffe2 and ONNX and combines them with PyTorch’s existing flexible, research-focused design to provide a fast, seamless path from research prototyping to production deployment for a broad range of AI projects. With PyTorch 1.0, AI developers can both experiment rapidly and optimize performance through a hybrid front end that seamlessly transitions between imperative and declarative execution modes. Data Scientists can develop models in PyTorch 1.0, which are saved in ONNX as the native format and directly use them in applications built on Windows ML and other platforms that support ONNX.
At Microsoft we believe bringing AI advances to all developers, on any platform, using any language, in an open and interoperable AI ecosystem, will help ensure AI is more accessible and valuable to all. Microsoft’s support for ONNX is an example of this – ONNX allows developers to choose the right framework for their task, framework authors can focus on innovative enhancements, and hardware vendors can streamline optimizations.
Azure Machine Learning Services provides support for a variety of frameworks including TensorFlow, Microsoft Cognitive
It is hard these days to not walk past something which is connected to the Internet in some way. These things are everywhere – desks, pockets, wrists, walls, kitchens, vehicles, factories, traffic stops, grocery shops… the list goes on and on. These things perform useful operations, gather data, and most importantly have built-in connectivity. There are endless possibilities to what can be achieved when the data from these things is securely captured, processed, and analyzed using the processing power, availability, and intelligence of the cloud. We want to explore these possibilities, with YOU!
Which is why we are inviting you to participate in the Azure IoT on Serverless hackathon for your chance to win* a piece of the $20,000 prize pool.
This online competition will run over the next few months, is open to anyone who wants to participate. In addition to winning cash prizes, this competition gives you an opportunity to be featured on the Azure blog.
All ideas are welcome, whether you want to work on that sensors-driven smart-home project you have been putting off, build a remote monitoring solution for a healthcare facility, create an intelligent system to streamline the manufacturing process of your production plant,
This post is authored by Xiaoyong Zhu, Anirudh Koul and Wee Hyong Tok of Microsoft.
How does one teach a machine to see?
Seeing AI is an exciting Microsoft research project that harnesses the power of Artificial Intelligence to open the visual world and describe nearby people, objects, text, colors and more using spoken audio. Designed for the blind and low vision community, it helps users understand more about their environment, including who and what is around them. Today, our iOS app has empowered users to complete over 5 million tasks unassisted, including many “first in a lifetime” experiences for the blind community, such as taking and posting photos of their friends on Facebook, independently identifying products when shopping at a store, reading homework to kids, and much more. To learn more about Seeing.AI you can visit our web page here.
One of the most common needs of the blind community is the ability to recognize paper currency. Currency notes are usually inaccessible, being hard to recognize purely through our tactile senses. To address this need, the Seeing AI team built a real time currency recognizer which can uses spoken audio to identify the currency that is currently in
We continue to expand the Azure Marketplace ecosystem. From April 1st to 15th, 20 new offers successfully met the onboarding criteria and went live. See details of the new offers below:
(Basic) Apache NiFi 1.4 on Centos 7.4: A CentOS 7.4 VM running a basic install of Apache NiFi 1.4 using default configurations. Once the virtual machine is deployed and running, Apache NiFi can be accessed via web browser.
Ethereum developer kit (techlatest.net): If you are looking to get started with Ethereum development and want an out-of-the-box environment to get up and running in minutes, this VM is for you. It includes the Truffle Ethereum framework, a world-class development environment.
xID: eXtensible IDentity (xID) is an open (standards based), modular (componentized architecture), secure (security built-in), and pluggable (adaptor-based integration approach) product built specially for delivering your organization’s identity management needs.
Qualys Virtual Scanner Appliance: Qualys Virtual Scanner Appliance helps you get a continuous view of security and compliance, putting a spotlight on your Microsoft Azure cloud infrastructure. It’s a stateless resource that acts as an extension to the Qualys Cloud Platform.
FileCloud on Ubuntu Linux: FileCloud