Microsoft Azure Machine Learning Studio Reviews

Our organization employs people with diverse professional backgrounds. We have sociology, mathematics, and statistics backgrounds. We employ these people within our data science team. They require a certain amount of programming skills.

The good thing about Azure Machine Learning is they have a drag and drop feature. You can use Azure Machine Learning designer for all of your data science teams.

Any non-programmer can adopt it. All he needs is statistics and data analysis skills.                                                                                             

I used Azure Machine Learning in a free trial and I had a complete preview of the service. A problem that I encountered was that I had a model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer. I didn't find any option to upload my model, so that I can create my own block and use it in Azure Machine Learning designer.

I believe this is a problem because sometimes you have your model created on some other device and you just have a file that you think can be uploaded to Azure Machine Learning and can be tested through a simple drag and drop tool.

We have been using Azure for three months. We have been exploring it for different use cases. 

I haven't used it long enough to have found any bugs in our current system. If there were bugs I would definitely report it on their website.

We didn't have any problems with the setup. It was pretty straightforward.

It's an easy tool. They have a good level of resources and we are pretty low with resources as far as data science is concerned.

Azure Machine Learning offers an opportunity for those who haven't been introduced to Azure programming. You can use the data analytics and their statistics skills to build and deploy data science solutions that can be beneficial for society and for different organizations.

I would rate it a seven out of ten.

My primary use is for machine learning applications.

The AutoML is helpful when you're starting to explore the problem that you're trying to solve. It helps automate some of the applications of the algorithm.

The AutoML feature is very basic and they should improve it by using a more robust algorithm. It lacks deep learning type algorithms but works great for the basic classification and regression models.

I have been using the Azure Machine Learning Studio on and off, or a few months. I have not used it consistently for a significant period of time.

From my experience over the past few months, I've found it to be pretty stable. I don't know how stable it would be if operationalized.

From my experience, I think that it's scalable.

Technical support is pretty good at answering questions, and the documentation is pretty clear to understand.

Compared to their big competitor, it's much easier to set up.

I work with a data architect who does the setup. I have not personally had to do it.

We are in the process of deciding which machine learning solution we want to use. I have been dabbling with Azure and we're deciding whether to implement it versus another cloud platform.

I haven't done any research into what features they have on their roadmap.

Overall, I think that this is a comparable product.

I would rate this solution a seven out of ten.

We primarily use the solution for data science.

The technical support of the solution is great. We have a contract with Microsoft and they are very good. 

The solution is very fast and simple for a data science solution. 

The pricing is very good.

The solution should be more customizable. There should be more algorithms. 

The solution needs more functionality.

We're at the beginning of the process and have only been using the solution for a few months.

The solution is very stable. We haven't had issues with bugs or glitches. We haven't experienced any crashes.

The solution is extremely scalable. This is because it's on the cloud. If a company needs to scale up they can do so quickly and easily.

At the moment, we have five employees using the solution. They are data scientists and engineers.

The solution offers very good technical support. Microsoft is well represented here in France. We've been very satisfied with support so far.

Previous to this solution, we had an improvised product. It wasn't a native cloud solution. We ended up choosing Azure Machine Learning because Azure is our management product. It made it easy for us to switch to the cloud.

The initial setup was very easy because it's a cloud solution. With the cloud option, you just subscribe, and you are ready to go in a few minutes.

I would recommend the product. It's a solution that can cover all the processes from data preparation to mobilization data while serving the clients and production. 

I'd rate the solution eight out of ten.

We primarily use this solution for data analytics and model building.

The interface is very intuitive.

It is very well organized and the components can be utilized through drag-and-drop.

The data preparation capabilities need to be improved. Using this product, I can not prepare the data very much and this is a bottleneck in machine learning.

There are some features that are not supported, so I have to use either Python or R to accomplish these tasks.

I have been working with the Azure Machine Learning Studio for between six and seven years.

Up to this point, we have not faced much in terms of issues with stability.

Scalability-wise, we have not had to deal with any limitations. The only problem is that when certain options are not there, we have to use Python or R to handle those tasks.

We have not faced any problems so I have not spoken with technical support.

The initial setup is very straightforward. It is not difficult to do.

I feel that this is a great solution. Even for people from the business side, this is a very good product. It is so intuitive that all of the information is there. The interface takes care of the most complex part, which has to do with the modeling. 

I would rate this solution a nine out of ten.

This solution can be used for data pre-processing, interactive data analysis, automated training, and pre-processing pipelines.

The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps.

In the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces. This would be something that would be helpful. Additionally, a better version for traceability functionality regarding data would be beneficial.

I have been using this solution for approximately six months.

The solution is stable.

I have found Microsoft Azure Machine Learning Studio scalable.

We have approximately eight people using the solution in my organization.

I have previously used Databricks. We switched to this solution because it provides better automation capabilities, easier to use external code, and allows the use of other tools, such as Docker containers.

The installation is easy. However, there is a bit more to do than with the installation of Databricks. The time it takes for the installation is approximately one day with a two-person team.

We use one engineer for the implementation and maintenance of the solution.

There is a license required for this solution.

I would recommend this solution to others.

I rate Microsoft Azure Machine Learning Studio a seven out of ten.

I am using Microsoft Azure Machine Learning Studio for my personal use.

I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model.

I think they should improve two things. They should make their user interface more user-friendly. Integration could also be better. Because Microsoft Machine Learning is a Microsoft product, it's fully integrated with Microsoft Azure but not fully supported for other platforms like IBM or AWS or something else.

I have been using Microsoft Azure Machine Learning Studio for almost two years.

Microsoft Azure Machine Learning Studio is a stable solution.

The initial setup is straightforward. I think it's a cloud service, and whenever I create a new workspace, it takes me around five to ten minutes to deploy it.

I implemented this solution.

I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs.

I would recommend this solution to new users. It's easy for people to use.

On a scale from one to ten, I would give Microsoft Azure Machine Learning Studio a nine.

in-house translation, time series and computer vision applications;  create models from scratch and just play around with data visualization.

Azure's AutoML feature is probably better than the competition.

The interface is a bit overloaded.

I've been using Azure ML Studio for about three months. 

Azure ML Studio has the same scalability as other similar solutions.

I haven't used Microsoft Azure support for this so far.

also using Amazon and Google solutions. In terms of performance, I think they're pretty much the same. Amazon SageMaker is a bit more mature.  Google Colaboratory and Vertex AI have better UI

Setting up ML Studio is very straightforward because it's a cloud thing. 

I rate Microsoft Azure Machine Learning Studio seven out of 10. I would definitely recommend it to customers. The autoML, in particular, has some very advanced features.

My primary use case is for supervised and unsupervised learning models.

The most valuable feature is its compatibility with Tensorflow.

In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data.

I've been using this solution for a year.

The stability is questionable, given that Microsoft will be retiring the classic version of this product in 2024, and it's unclear how this will affect projects created on the classic version.

This solution is scalable.

The initial setup was straightforward, though you do need some experience with Azure administration in order to install it.

I evaluated Amazon SageMaker, which is a bit more advanced than Azure Machine Learning, with more functionalities and only a slightly higher price.

I would rate this solution as seven out of ten.