Microsoft Azure

One of the best Public cloud platforms available today, we use it for Iaas, Paas as well as SaaS. Easy to learn, good guiding UI. I highly recommend using the following. Mostly we have used it for containerization, storage, and compute, we got a very good experience after using it

We use Microsoft Azure for cognitive services like natural language processing, computer vision, and machine learning services for built-in open-[source] frameworks like Tensorflow, MXNet, Keras, etc. It is used based on customers' needs, as we are in the service industry. It helps with scaling, security, and reliability.

Our company (200+) recently transitioned over to [Microsoft] Azure to alleviate the "clutter". We were outsourcing various functions, causing confusion and conflict. Having changed over to 1 concise platform containing all of our needs, has cleared the bottleneck we have been fighting for years. It has been a smooth transition as well with minimal to zero impact.

[Microsoft] Azure has a large amount of services and capabilities, but we're using it primarily for Identity (Azure Active Directory), Networking (Application Gateways, ExpressRoute), as well as some basic Compute (Virtual Machines) and some web and storage. Also, as big Office 365 customers, we use [Microsoft] Azure in conjunction with some of these services such as storage (backups) and some media services (videos). Additionally, some Azure AI and security services are used in conjunction with some of the O365 services for DLP (digital loss protection) and other security services. Of course, we also use the Azure Portal to manage all of this. All in all it is a very nice and integrated (and expandable) solution for a total and cohesive set of common business problems like we just described. We flat out could not do much of what we do now without Azure AD (MUCH better than on-premises Active Directory). There are some learning curves and potentially some cost barriers, but again, since the "total" Azure solution can be implemented in parts and expand as needed, it still makes an excellent way to address immediate problems (whatever you feel is most important) while simultaneously positioning your business to succeed in the future as you are able to add on services and shift older legacy solutions to the cloud.

Microsoft Azure provides seamless data access on the cloud. Currently in our organization this is being used department wide. We are using this for hosting our applications on virtual machines and managing the same on the cloud. As well for data storage purposes. Platform hosting and resources availability is excellent. It's highly secure and infrastructure recovery is pretty fast and seamless. Large scale data storage is no longer an issue. Virtual machines availability is pretty cool.

I have used Microsoft Azure previously to run some machine learning algorithms that processed a large amount of data and made predictions. This allowed us to improve someone's performance.

We use our Azure storage accounts for blob storage and SMB shares. They don't work on all ISP providers or hotspots. They work on Verizon fiber and cellular hotspots but not one of our local ISPs. We use the SQL server and database services. I really like how easy it is to scale them up and down, live.
We also have a website app meant for internal use and use the SendGrid account connector. It is really nice to have easy white-listing for everything: websites, databases, etc. that we can trust. We host everything here instead of making servers VPN into our office.
Additionally, we have a site-to-site VPN between our offices and Azure. That works well and enabled us to turn off much of our public access.
We use the built-in VM backup tools and those have been really easy to use right there on Azure. Also, the snapshots automatically built into databases we have used several times. It creates a new database from a snapshot from every few hours from up to almost 30 days ago and then you just copy over whatever data you need.
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[It's being used] Across a specific department; allows us to model data and to carry out a selection of machine learning tasks around data from our reports.

Microsoft Azure has been our platform of choice since 2015. We initially implemented a backup solution but grew into the environment as we required more and more resources. Now we take advantage of the ecosystem consisting of websites, SQL servers, virtual machines just to name a few.

As a marketing services provider, we have lots of legacy applications that were built atop the .NET framework. As we expanded to offer cloud services to better serve our clients, we decided to go with Microsoft Azure as the PAAS partner. Not only does it allow us to use Microsoft servers in the backend but also Linux as part of our backend infrastructure. We use SQL Database servers for our data layer and it was an easy choice for us to leverage Microsoft Azure as the common platform.

Microsoft Azure is used by departments to manage larger data sets across entities. The software addresses the need for multiple users to have access to multiple different data sets simultaneously. The software makes this relatively easy by making Microsoft Azure similar to the user-friendliness of other Microsoft products. Users point their analytical tools at Azure for data visualization and analytics. Some analytics is also done in Azure itself.

We use select Microsoft Azure products and APIs to complete some of our more machine learning-oriented tasks off-premise. This includes modeling data we receive from magnetometer sensors, recognizing environmental effects in data during hardware testing, and some other backend-related tasks like modeling traffic and parking behavior. Using these products allows us to move tasks that require lots of processing power to cloud products that are optimized for this purpose.

We are using Azure for our research on Learning Analytics for the University. For now, it is being by only a department but we plan to deploy it in other departments as well. We are mostly using ElasticSearch and Machine Learning service since we need it for our predictive analysis and prescriptive analysis and also trying to use for our apps.

For cloud deployment, for continuous integration. Saving TCO of IT infrastructure, reduce business downtime. Having satisfied customers. Azure is a reliable cloud service for whom would like to achieve 0 downtimes. We are going to explore what Azure can do on Machine Learning and AI for the trading platform and predict the business impact.

We are using Azure to host our mortgage application.
The reason to choose Azure was:
a) We can't afford to have any downtime of our application
b) At any point of time, user should need feel that application is behaving slow.
c) We would like to focus more on our line of business application instead of investing time in monitoring and deployment.

Windows Azure is being used to host a QR Code application in the cloud.

Looking at lower costs alternatives to maintaining our own data center assets for our commercial buisness applications.

I used Azure Websites for my service's ASP.Net MVC website. I published the website from Visual Studio 2012 & 2013 and it worked like a charm.

I used an Azure SQL Database for the website's membership (login) data. Again, worked great.

I configured the SQL Database for weekly automatic export to an Azure Storage account for backup purposes. The export ran without issue 99.999% of the time. There were a couple instances, over the course of a couple years, in which the export failed and I received an email to this effect. In these cases, I went into the Azure portal and reran the export manually.

I used Azure Service Bus Queues to queue email jobs. Email jobs were generated by the website when users used a "send invitation" function to email an invite to friends. Email jobs were also generated by a separate back end process that ran on-premises, and not on Azure. Another on-premises back end process pulled the email jobs from the Azure Service Bus queue and sent out the emails. The Azure Service Bus queue worked great and was very solid. The Service Bus Queue API used to enqueue and dequeue jobs took a little time to understand, but beyond the learning curve, I had no problems with it. Very solid.

I would highly recommend Azure. Besides the solid performance of the services that I used, they are constantly pushing prices down, evolving the existing services, and rolling out new services. New announcements of lower prices and service improvements come nearly every two weeks. It is a very impressive operation, top to bottom, from the physical data centers to the website portal that you use to interact and configure your services.