Microsoft Azure Reviews

We are using the solution for applications.

There are several products within Azure.

I have been using the solution for four years.

The solution is stable in my experience.

The solution is scalable.

The service is good.

The cost structure is like most cloud-based services. The solution could be cheaper, it is always better to be cheaper.

I recommend this solution.

I rate Microsoft Azure a ten out of ten.

Public Cloud

We work with a lot of cloud-related products, including Azure.

The most valuable feature is cloud-based storage.

All of the cloud-based features are helpful.

I would like to see better integration with other products.

We have been using Microsoft Azure for a few years.

I have not heard any complaints about the stability of Azure. Most likely, we will continue to use it in the future.

From what I understand, it is scalable. We are quite a big company and as such, we have quite a lot of users.

I have not been in contact with technical support.

In my experience, it has always been Microsoft Azure.

Azure is a good product and I can recommend it to others.

I would rate this solution an eight out of ten.

Public Cloud

Microsoft Azure

Installation of SQL Server, Cloudera Hadoop, and MySQL. The performance has been great.

The benefit is the time to generate all these machines. It's in the cloud so I don't feel it.

I come from the DBA side so for me it's the easy maintenance; backups are very easy as well.

I would like to see more databases on the cloud, what they call today Big Data should be there. I think it's going in the right direction.

Stability is great.

Scalability is also great.

Personally, I have not used support, but the guys who do are happy with it.

Cloudera.

When choosing a vendor what's important to me are stability, a lot of features, variety, and good support.

My advice would be, try it using the free trial and I am sure you will be satisfied and take it.

Yet another powerful and reliable product from Microsoft. Windows Azure is a very reliable cloud computing platform, coupled with Microsoft data centers. More positives are that it can be used with non-Microsoft based data centers. It is fast and secure. It stands first among all other cloud platform providers.

Microsoft is not an innovator for this cloud computing platform. Still some of its products, like Dynamics GP 2013 online, are not compatible with iPad, iPhone, and some Android based devices.

I love Microsoft's products because they are simple yet powerful and secure.

We used Microsoft Azure mainly for repositories and management.

The most valuable feature of Microsoft Azure is its ease of use.

Microsoft Azure could improve by being more secure.

I have been using Microsoft Azure for approximately two years.

Microsoft Azure is a stable solution.

The company that I am working for had 100 users using Microsoft Azure.

I did not implement the solution myself, another team in my company did it.

I would recommend Microsoft Azure to others, it is a good solution.

I rate Microsoft Azure a nine out of ten.

On-premises

Some features of Azure are very important for us. You can control access for your company, it's not complicated managing the solution either. I think the solution is very stable, secure and the documentation Microsoft has on their website is complete. You can use it to find the solution's problems or implement solutions. 

An issue I encountered was around the integration of Identity. I lost some important information and I would like to see improvements with this. 

I have been using Microsoft Azure for approximately 2 years now

Azure is very easy to manage

We are a Microsoft partner and implement in-house.

The pricing depends on the solutions I want to use, but I think the price is good because there are many free features in the solution. If you need something more advanced, like Azure AD Premium, you have other features available like risk management.

I would rate Azure a nine out of ten. 

Public Cloud

Microsoft Azure

The most valuable feature is the interface.

The price could be improved as well as the interface speed, and technical support.

I have been using Microsoft Azure for four years.

This solution is stable.

Microsoft Azure is a scalable product.

We have more than ten users.

I am not satisfied with the support. It seems that they outsource to different companies with people who don't even know the basics. 

You get asked several questions but never get any resolutions.

The initial setup was easy and it only takes a few minutes to install.

The cost is a little high and can be more competitive.

Microsoft Azure is a good service and I recommend it.

I would rate this solution an eight out of ten.

Hybrid Cloud

The Canvas flow interface brings a very nice functionality that improves self-learning.

Selecting and applying a machine learning algorithm is not a click-and-run process. In this case, clustering our data has helped us to find patterns and trends that were not visible using conventional (internal) classification.

Stronger R integration in a circular fashion (Azure->R->Azure). Nowadays, it has an unidirectional bias.

I've used Machine Learning Studio for the last six months.

We've had no issues with the deployment.

By using a free account for cloud services can bring you "queued" status, but normally it runs smoothly. The quota defined for free account is enough for a variety of experiments and none of the features are blocked.

We've had no issues with the scalability.

Customer Service:

As a cloud service, online chat and email service are available, but the community forums are the best place to solve issues.

Technical Support:

We have a support maintenance agreement for Windows/Office.

There are a few competitors in place, and their number is increasing, but friendliness is the strong point of AzureML, comparing to Amazon AWS or BigML.

It does not involve any setup, just having an Outlook or Microsoft account.

Using a free account leads to offline projects. For online projects those issues must be evaluated according to client side company's environment.

Machine learning is just one part of the whole data science cycle. Big data (streaming, video, etc.) or deep learning needs must be addressed with additional tools. But for prediction/classification this is a fantastic tool.

Below is a PCA graph generated by K-means training model and its Qlik Sense panel. The idea is to segment unlabeled data based on numerical features in order to find common patterns that can be grouped, named clusters.