Microsoft Azure Machine Learning Studio Reviews

I wouldn't say it's necessarily about liking everything about the platform entirely. It's more about what do we want? In terms of machine learning, there are times that we have to get into it and customize it, etc. We can use the ready-made models that are available without really having to code encrypt them with our bitcoin code — our model doesn't need to be too complex. Deployments and everything, in general, can be automated from a CI/CD perspective as well.

I really can't see where it needs much improvement. My experience is only half-matured and is still maturing.

I don't think we have reached the stage where the customer has enough cohesion to really complain about anything. Also, a Microsoft team is personally involved which really simplifies the process.

In the machine learning world, when you are defining the model, typically people go for an interesting library of algorithms that are available. It's an imperfect scenario. The world is not as ideal as we think: how we draw a mathematical or theoretical formula is not exactly as it seems. With encryption, this uncertainty is actually much higher — that's why you need to tweak your mathematical formula or completely customize it. For this reason, my team has a development platform where they can customize code when it fails.

I have been using this solution since June.

Regarding the stability and scalability — so far so good; however, we're still exploring quite a bit. It's too early to really comment because the customer has already paid. They've just started their journey. We are yet to explore exactly what and how they want to use it. 

So far, we haven't had a situation where we have needed to raise a ticket for support on a technical front.

Currently, we're handling any issues internally because we're still in the initiation stage. It's going to take some time for us to really get our hands into it, but so far it's been a really good experience. Based on various conversations that I was part of, I think our customer really appreciates the support coming from our people.

 Compared to similar solutions, Microsoft Azure Machine Learning Studio is quite new so the initial setup wasn't much of a challenge. The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team.

I would Definitely recommend Azure Machine Learning Studio — no doubt about it, it's a full-contact solution. Having said that, it really depends on the customer's appetite and what they're comfortable with. For example, I have interacted with people who prefer a basic Google cloud platform — from an AML perspective, they just feel like it's primarily Google. Not because of AML per se, it's more from a data storage perspective, which in this case, works better.

Personally, I come from a VFA site in the financial sector. Over there, the customers are really conscious about hosting their station or their data, especially on the cloud. Typically, they are very restricted because they are not comfortable hosting customer data on the cloud. This is where I think Azure or Google or even AWS fall short — they don't play any role there. Because of this, people actually customize their solutions or model them to fit their custom sites and customer-based solutions. 

Overall, I would give this solution a rating of seven. It's a great option if you are fairly new and don't want to write too much code. As long as the model is not too complex, it's a pretty easy solution to roll out.

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.

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're using the solution in order to give the customer a 360 degree view. Also, we use it if clients want to do machine learning with AI at a more reasonable cost.

Right now, we are just testing the customer insights from Microsoft.

The UI is very user-friendly and that AI is easy to use.

Usually, we also use the machine learning studio to build up the data logistics in machine learning.

On the customer side, the solution should do more to push companion marketing.

When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers.

The solution should simplify switching between platforms in the studio.

I've been dealing with the solution for two years.

I've only used the solution a couple of times. I haven't noticed any bugs and when I used it, it worked quite smoothly.

I don't have enough knowledge about the solution's scalability to be able to comment on it. Right now, we have about 5,000-6,000 users on the solution. Most are data scientists, and IT admins.

I've personally been in touch with technical support and I found them quite helpful.

I've only ever worked with Microsoft Azure. We didn't previously use a different solution.

The initial setup is very straightforward.

Our clients do the implementation with the help fo consultants like us.

The pricing and licensing are difficult to explain to clients. Their rationale for what things cost and why are not easy to explain.

I'm a consultant. Our company is partners with Microsoft.

Users will find it easy to get into Azure. Even if they aren't always in touch with Azure, they'll find themselves in touch with the dynamic field. Users have to get into Azure because once they get into the cloud, they should have some basic understanding of Azure itself.

I'd rate the solution eight out of ten. However, I don't know their competitors, so I can't really compare them to others on the market.

Azure Machine Learning Studio works with our ERP solution.

The most valuable feature is data normalization.

The data cleaning functionality is something that could be better and needs to be improved.

There should be special pricing for developers so that they can learn this solution without paying full price.

I have been using Azure Machine Learning Studio for more than two years.

This is a stable solution.

I believe that it is scalable. At this time, we have not more than ten users. These include programmers, as well.

I have been in contact with technical support and they are good. I am happy with their response time.

The initial setup is straightforward and not too complex.

We did the implementation by ourselves.

From a developer's perspective, I find the price of this solution high. If somebody wants to learn how to use this platform then they have to spend money doing it. I know people who are interested in learning it but do not want to pay the full cost.

Microsoft Azure Machine Learning Studio is a good solution that would recommend to others, but I would like to see more support and more information available for developers.

I would rate this solution an eight out of ten.

Exploration of connections between biodata and psychometric test results.

Visualisation, and the possibility of sharing functions.

Operability with R could be improved.

The first time that I used this tool was in a project related to bike usage in the city of Boston. This project was part of a course that I concluded some months ago. In this project I used components to read data, for exploratory analysis, for steps of data munging, to split data, select hyperparameters, and some machine learning algorithms. In some steps I needed to insert R modules to apply some data transformation.

The target of this exercise was to predict bike usage in a day.

With this tool we could have all benefits of a cloud environment, such as scalability and access to machine-learning applications. These features are very important when you have large datasets and critical applications.

• It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component.
• When you import the dataset you can see the data distribution easily with graphics and statistical measures.
• Easy to deploy and provide the project like a service.

For my project/exercise, this tools was perfect. I would like to see modules to handle Deep Learning frameworks.

No issues with stability.

No issues with scalability.

I didn’t need to use the support, but this tool has great documentation.

Nowadays I use Python (Anaconda and Jupyter Notebook) and R (RStudio) to create my solutions and machine-learning models.

It was very simple and straightforward. It is really simple to start building a project.

There are two kinds of licenses, Free and Standard.

Free

• 100 modules per experiment.
• 1 hour per experiment.
• 10GB storage space.
• Single Node Execution/Performance.

Standard – $9.99/seat/month (probably a data scientist)

• $1 per Studio Experimentation Hour. You will pay according to the number of hours your experiments run.
• Unlimited modules per experiment.
• Up to seven days per experiment, 24 hours per module.
• Unlimited BYO storage space.
• On-premises SQL data processing.
• Multiple Nodes Execution/Performance.
• Production Web API.
• SLA.

You will be able to create your machine-learning project and extract insights from it just by dragging and dropping components and adjusting some parameters. This tool is very user-friendly, so without a lot of programming skills you can build machine-learning projects. 

If you need more control over machine-learning modules you will need to add R or Python modules to create a customized machine-learning model.

My  primary use of ML Studio is to experiment with different algorithms and learn the techniques of machine learning. In the meantime, I have developed a few models related to finance. One of the predictive models I designed was an Invoice Discrepancy Prediction model using a Multiclass Neural Network algorithm. This model predicts if an invoice will have a variance of some sort when checked against the purchase order, before the payments are to be processed.

Thanks to the model I designed, the productivity of processing invoices has increased by over 11%, because the team members only verify invoices that are discrepancy-free now.

• Split dataset
• variety of algorithms
• visualizing the data
• drag and drop capability 

are the features I appreciate most. 

The capability to model the data by finding empty cells and filling missing values by deriving the median and more, are great features that makes the job way easier.

I personally would prefer if data could be tunneled to my model through a SAP ERP system. It also needs features of Excel, such as Pivot Tables, integrated.