Our Azure customers have three common needs from their cloud support plan:
A fixed monthly cost that is affordable and simple to forecastFast response time for critical casesA plan that covers their entire organization and eliminates guesswork in how many support plans are needed and who can use them
We are pleased to announce important updates for Azure Standard support. With these changes, Azure now offers the most cost effective and predictable support offering amongst major cloud providers.
Azure Standard support now includes:
A significant price drop to a fixed cost of $100 USD per month, so forecasted support costs are completely predictableFaster initial response time, now at 1 hour for critical casesContinuing our current offering of unlimited 24×7 technical and billing support for your entire organization
Click here for more details, eligibility, and frequently asked questions.
Azure is continuously improving and expanding the range of options to help you accelerate your cloud journey. From the built-in Azure Advisor service that provides free, proactive, and personalized best practice recommendations, to direct connection with Azure engineers through multiple levels of Azure support. There are also unique support options for different types of
We are pleased to announce preview availability of SDKs for the Cognitive Serivces – Bing Search APIs. Currently available as REST APIs, the Bing APIs v7 now have SDKs in four languages: C#, Java, Node.js, and Python. These SDKs include offerings such as Bing Web Search, Bing Image Search, Bing Custom Search, Bing News Search, Bing Video Search, Bing Entity Search, and Bing Spell Check.
Here are some of the salient features of these SDKs:
Easy to use and highly flexible in adjusting your basis application scenario. Encompass all the API v7 functionalities, languages, and countries. Reduce assembly footprint through individual SDK for each Bing offering. Enable development in C#, Java, Node.js, and Python. Provide ability to use the new/existing Bing APIs access keys, both free and paid. Well documented through samples and parameter references. Supported through Azure and other developer forums. Opensource under MIT license and available on GitHub for collaboration.
Getting Started with Bing SDKs
For C#, both NuGet packages and SDKs are available for individual Bing offerings. The best place to start is with C# samples. These samples provide an easy to follow step-by-step guide on running various application specific scenarios through corresponding NuGet packages.
The ability to run Spark on a GPU enabled cluster demonstrates a unique convergence of big data and high-performance computing (HPC) technologies. In the past several years, we’ve seen the GPU market explode as companies all over the world integrate AI and other HPC workflows into their businesses. Tensorflow, a framework designed to utilize GPUs for numerical computation and neural networks has skyrocketed into popularity, a testament to the rise of AI and consequently the demand for GPUs. Simultaneously, the need for big data and powerful data processing engines has never been greater as hundreds of companies start to collect data in the petabyte range.
By providing infrastructure for high performance hardware such as GPUs with big data engines such as Spark, data scientists and data engineers can enable many scenarios that would otherwise be difficult to achieve.
Along with the recent release of our latest GPU SKUs, I’m excited to share that we now support running Spark on a GPU-enabled cluster using the Azure Distributed Data Engineering Toolkit (AZTK). In a single command, AZTK allows you to provision on demand GPU-enabled Spark clusters on top of Azure Batch’s infrastructure, helping you take your high performance implementations that are usually
We on the Azure Site Recovery product team are consistently striving to simplify the business continuity and disaster recovery to Azure solutions for our customers. With the latest release of the Azure Site Recovery service for VMware to Azure, we bring a new, intuitive and simplified getting started experience, which gets you setup and ready to replicate virtual machines in less than 30 minutes!
What is new? Open Virtualization Format (OVF) template-based configuration server deployment
Open Virtualization Format (OVF) template is an industry standard software distribution model for virtual machine templates. Starting January 2018, configuration server for the VMware to Azure scenario will be available to all our customers as an OVF template.
With the OVF template, we ensure that all the necessary software, except MySQL Server 5.7.20 and VMware PowerCLI 6.0, is pre-installed in the virtual machine template, and once the template is deployed in your vCenter Server, the configuration sever can be registered with the Azure Site Recovery services in less than 15 minutes.
Here is a quick video that walks you through the new onboarding experience.
Read more on how to deploy the configuration server template to your VMware vCenter Server / ESXi host.
Azure Site Recovery is a vital part of the business continuity strategy of many Azure customers. Customers rely on Azure Site Recovery to protect their mission critical IT systems, maintain compliance, and ensure that their businesses aren’t impacted adversely in the event of a disaster.
Operationalizing a business continuity plan and making sure that it meets your organization’s business continuity objectives is complex. The only way to know if the plan works is by performing periodic tests. Even with periodic tests, you can never be certain that it will work seamlessly the next time around due to variables such as configuration drift and resource availability, among others.
Monitoring for something as critical should not to be so difficult. The comprehensive monitoring capabilities within Azure Site Recovery gives you full visibility into whether your business continuity objectives are being met. Not just that, with a failover readiness model that monitors resource availability and suggests configurations based on best practices, it also helps inform how prepared you are to react to a disaster today.
So, what is new in this experience?
Enhanced vault overview page: The new vault overview page features a dashboard that presents everything you need to know to
I am pleased to announce a new webcast series showcasing innovative technology partners who have built solutions on top of the Azure Storage infrastructure. Microsoft has always been committed to our partner ecosystem and we are especially proud of the work we have done on the Azure Storage team. Over the last two years we have witnessed an impressive increase in the number of solutions that integrate with, or are built on top of Azure Storage. All of these solutions are capable of helping our customers take advantage of Azure services and achieve tangible benefits for their businesses. It is all about our customers and helping you achieve your goals.
So what will you see and learn about during this series? Learn to use solutions you already have, from the vendors you trust, while extending your data center to Azure and building Cloud native solutions with your data. How to synchronize or migrate data to Azure Storage and leverage it with On-Demand Azure Services like: VMs High Performance Computing App Services Containers Media Services Databases Analytics Machine Learning Cognitive Services Manage explosive data growth in your organization. Worried about GDPR? Meet compliance and legal discovery requirements. End the cycle
We are excited to announce the public preview of Azure Zone Redundant Storage (ZRS). ZRS greatly simplifies development of highly available applications by storing three replicas of your data in different Availability Zones, with inserts and updates to data being performed synchronously across these Availability Zones. This enables you to continue to read and write data even if the data in one of the Availability Zones is unavailable or unrecoverable. ZRS is built over Availability Zones in Azure which provide resilience against failures through fault-isolated groups of datacenters within a single region.
Zone Redundant Storage should be considered for applications where regional availability is critical and downtime is not acceptable, and both read and write access are required at all times.
With the release of the ZRS public preview, Azure offers a compelling set of durability options for your storage needs including ZRS for intra-region high availability, locally-redundant storage (LRS) for low-cost single region durable storage, and geo-redundant storage (GRS) for cross-region redundancy for disaster recovery scenarios with read access geo-redundant storage (RAGRS) offering additional read accessibility.
The ZRS preview will initially be available in the following regions with more to follow. Please check our documentation for the latest list
This post is also authored by Kiran Madnani, Principal PM Manager, Azure Infrastructure Management and Snehith Muvva, Program Manager II, Azure Infrastructure Management.
We are happy to announce that the IT Service Management Connector (ITSMC) for Azure is now generally available. ITSMC provides bi-directional integration between Azure monitoring tools and your ITSM tools – ServiceNow, Provance, Cherwell, and System Center Service Manager.
Customers use Azure monitoring tools to identify, analyze and troubleshoot issues. However, the work items related to an issue is typically stored in an ITSM tool. Instead of having to having to go back and forth between your ITSM tool and Azure monitoring tools, customers can now get all the information they need in one place. ITSMC will improve the troubleshooting experience and reduce the time it takes to resolve issues. Specifically, you can use ITSMC to:
Create or update work-items (Event, Alert, Incident) in the ITSM tools based on Azure alerts (Activity Log Alerts, Near Real-Time metric alerts and Log Analytics alerts) Pull the Incident and Change Request data from ITSM tools into Azure Log Analytics.
You can setup ITSMC by following the steps in our documentation. Once set up, you can send Azure alerts to ITSM
In September 2017 we introduced Azure Availability Zones, enabling resiliency and high availability for mission-critical workloads running on Azure. Today, we are excited to announce the public preview of Zone Redundant Virtual Machine Scale Sets, bringing the scalability and ease of use of scale sets to availability zones.
Deploying your infrastructure across zones has never been easier. You just specify the availability zones you would like to use for your scale set. It’s as simples as:
az vmss create -n <name> -l <location> –image <image-name> -g <resource-group-name> –zones 1 2 3
With Zone Redundant Virtual Machine Scale Sets, your Virtual Machines are automatically spread across availability zones. You don’t need to worry about distributing VMs across zones, choosing which VMs to remove when scaling in, etc. Zone Redundant Virtual Machine Scale Sets support the same capabilities as Regional Virtual Machine Scale Sets, including but not limited to:
Azure Autoscale Azure Virtual Machine Extensions Marketplace and Custom Images Attached Data Disks Azure Application Gateway Azure Load Balancer Standard
Please note that during preview, some of these capabilities might not be fully zone redundant.
With scale sets, it’s easy to build big compute, big data, and containerized workloads. With zones it’s
This post is authored by John Ehrlinger, Data Scientist at Microsoft.
Microsoft has recently launched Azure Machine Learning services (AML) to public preview. The updated services include a Workbench application plus command-line tools to assist in developing and managing machine learning solutions through the entire data science life cycle. An Experimentation Service handles the execution of ML experiments and provides project management, Git integration, access control, roaming, and sharing of work. The Model Management Service allows data scientists and dev-ops teams to deploy predictive models into a wide variety of environments. Model versions and lineage are tracked from training runs to deployments while being stored, registered, and managed in the cloud.
Once AML Workbench is installed, the app connects to a Gallery of prebuilt real world data science scenario projects to help new users explore Azure ML, as well as give users a jump start on their specific data science scenarios.
The AML gallery currently contains two predictive maintenance example scenarios:
A PySpark implementation using a random forest of decision trees classifiers:
A deep learning approach using an LSTM classifier:
This post is written specifically to prepare users interested in using their own data to deploy customized predictive maintenance scenarios.