Microsoft Azure Machine Learning Studio OverviewUNIXBusinessApplication

Microsoft Azure Machine Learning Studio is the #1 ranked solution in top AI Development Platforms and #3 ranked solution in top Data Science Platforms. PeerSpot users give Microsoft Azure Machine Learning Studio an average rating of 7.6 out of 10. Microsoft Azure Machine Learning Studio is most commonly compared to Databricks: Microsoft Azure Machine Learning Studio vs Databricks. Microsoft Azure Machine Learning Studio is popular among the large enterprise segment, accounting for 71% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 15% of all views.
Microsoft Azure Machine Learning Studio Buyer's Guide

Download the Microsoft Azure Machine Learning Studio Buyer's Guide including reviews and more. Updated: November 2022

What is Microsoft Azure Machine Learning Studio?

Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.

Microsoft Azure Machine Learning Will Help You:

  • Rapidly build and train models
  • Operationalize at scale
  • Deliver responsible solutions
  • Innovate on a more secure hybrid platform

With Microsoft Azure Machine Learning You Can:

  • Prepare data: Microsoft Azure Machine Learning Studio offers data labeling, data preparation, and datasets.
  • Build and train models: Includes notebooks, Visual Studio Code and Github, Automated ML, Compute instance, a drag-and-drop designer, open-source libraries and frameworks, customizable dashboards, and experiments
  • Validate and deploy: Manage endpoints, automate machine learning workflows (pipeline CI/CD), optimize models, access pre-built container images, share and track models and data, train and deploy models across multi-cloud and on-premises.
  • Manage and monitor: Track, log, and analyze data, models, and resources; Detect drift and maintain model accuracy; Trace ML artifacts for compliance; Apply quota management and automatic shutdown; Leverage built-in and custom policies for compliance management; Utilize continuous monitoring with Azure Security Center.

Microsoft Azure Machine Learning Features:

  • Easy & flexible building interface: Execute your machine learning development through the Microsoft Azure Machine Learning Studio using drag-and-drop components that minimize the code development and straightforward configuration of properties. By being so flexible, the solution also helps build, test ,and generate advanced analytics based on the data.
  • Wide range of supported algorithms: Configuration is simple and easy because Microsoft Azure ML offers readily available well-known algorithms. There is also no limit in importing training data, and the solution enables you to fine-tune your data easily, saving money and time and helping you generate more revenue.
  • Easy implementation of web services: Simply drag and drop your data sets and algorithms, and link them together to implement web services. It only requires one click to create and publish the web service, which can be used from any device by passing valid credentials.
  • Great documentation: Microsoft Azure provides full stacks of documentation, such as tutorials, quick starts, references, and many other resources that help you understand how to easily build, manage, deploy, and access machine learning solutions effectively.

Microsoft Azure Machine Learning Benefits:

  • It is fully integrated with Python and R SDKs.
  • It has an updated drag-and-drop interface, generally known as Azure Machine Learning Designer.
  • It supports MLPipelines, where you can build flexible and modular pipelines to automate workflows.
  • It supports multiple model formats depending upon the job type.
  • It has automated model training and hyperparameter tuning with code-first and no-code options.
  • It supports data labeling projects.

Reviews from Real Users:

"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.” - Channing S.l, Owner at Channing Stowell Associates

"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.” - Chris P., Tech Lead at a tech services company

"The UI is very user-friendly and the AI is easy to use.” - Mikayil B., CRM Consultant at a computer software company

"The solution is very fast and simple for a data science solution.” - Omar A., Big Data & Cloud Manager at a tech services company

Microsoft Azure Machine Learning Studio was previously known as Azure Machine Learning, MS Azure Machine Learning Studio.

Microsoft Azure Machine Learning Studio Customers

Walgreens Boots Alliance, Schneider Electric, BP

Microsoft Azure Machine Learning Studio Video

Archived Microsoft Azure Machine Learning Studio Reviews (more than two years old)

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Owner at Channing Stowell Associates
Real User
Top 10
Has the ability to do templating and transfer it so that we can do multiple types of models and data mining
Pros and Cons
  • "The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout."
  • "In terms of improvement, I'd like to have more ability to construct and understand the detailed impact of the variables on the model. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" it wasn't great."

What is our primary use case?

Developing and operationally implementing a powerful lead scoring model for a major Multiufamily developer and operator of apartment properties throughout major western states. The work included 3 years of data across over 60 properties with more than 500,000 leads and 3 million transactions.

How has it helped my organization?

Increased sales force productivity by permitting them to prioritize activity during peak leasing periods on those leads most likely to close

What is most valuable?

The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.

We were working across a number of internal departments as well as some outside departments and this solution made it extremely easy to communicate across functional area because it was all in flow chart and data form so that if somebody had an issue, like changing the data set or something like that, they could point right to it and we could get that handled and incorporated into the model. It's extremely efficient on the computer. We had to do a number of resets on the data in the model and to be able to turn things around and validate the model and the new set in two hours, was just incredible for me.

It was very robust. The ability to move the objects around so easily and then communicate is really its power. Then to be able to show it to the sales and senior management, in terms of what was employed and made it very easy to get my job done.

What needs improvement?

In terms of improvement, I'd like to have more ability to understand the detailed impact of the variables on the model and their interactions. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" Azure (at least my understanding of it) doesn't provide readily accessible tools to assess from a management perspective the impact of their changing a sinimized, the better.gle value - for instance in closing a lead, decreasing response time by 10%.

I recognize that the multivariate algorithms used from decision trees to neural nets do not readily provide the coefficients for each variable ala the older regression modeling approaches. My experience over my 50 years of developing and implementing predictive models has been that more than half the value of modeling lies in improving management's understanding of the process being modeled, often leading to major organization and operational structure changes. More ability to understand the variables impacting the end result being optimized would be very useful. 

Buyer's Guide
Microsoft Azure Machine Learning Studio
November 2022
Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: November 2022.
653,757 professionals have used our research since 2012.

For how long have I used the solution?

I have worked extensively with this solution for the last three years. 

What do I think about the stability of the solution?

I haven't had any problems with stability. 

What do I think about the scalability of the solution?

I didn't have any issues with the scale. we rapidly went from test to full implementation across all datasets.

How are customer service and support?

I never had to use technical support.

Which solution did I use previously and why did I switch?

I have used SPSS modeler (part of WATSON really) but because client was a Microsoft shop, I switched to Azure.

How was the initial setup?

I found the setup to be very easy. I've been doing this type of work for 50 years so the modern terminology isn't always the same as what I grew up with. It took me a while to understand that, but the setups were very easy. As with anything, the hardest part is always getting the data together, but the outside consultants had built up a very, very good data warehouse. The ability to manipulate the data and create variables was very nice.

THIS IS THE ONLY MODELING APPROACH THAT EVER WORKED THE VERY TIME I RAN IT!!

What's my experience with pricing, setup cost, and licensing?

Because client isa Microsoft shop, everything was Microsoft in terms of having solutions like Power BI and stuff like that. Azure is very useful and very inexpensive.

What other advice do I have?

The major advice I give is that clients must get the user,somebody who understands the business issues, to be deeply involved with it and the data transformation. Most people don't. And that's true for data science applications. We don't just follow the data in a big pile and remodel, we advance the process that we're modeling. Consider what transformations of the data you need to make it workable and usable.

Remember, over half the initial value of modeling is the strategic understanding provided re the importance of different variables to the model and hence the organizaion's performance. Very often the modeling identifies opportunities for changing structures, decision rules, etc. even prior to the model's actual implementation technically.

I would rate it a nine out of ten.

Which deployment model are you using for this solution?

Private Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Business transformation advisor/Enterprise Architect at a tech services company with 51-200 employees
Real User
Top 20
A low-code to no-code option that has more maturing to do
Pros and Cons
  • "It's a great option if you are fairly new and don't want to write too much code."
  • "The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team."

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

I have been using this solution since June.

What do I think about the stability of the solution?

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. 

How are customer service and technical support?

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.

How was the initial setup?

 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.

What other advice do I have?

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.

Disclosure: My company has a business relationship with this vendor other than being a customer: Integrator
PeerSpot user
Buyer's Guide
Microsoft Azure Machine Learning Studio
November 2022
Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: November 2022.
653,757 professionals have used our research since 2012.
Saurabh-Singh - PeerSpot reviewer
Head - Data Analytics at a consultancy with 51-200 employees
Real User
Interface is well-organized and intuitive to use
Pros and Cons
  • "The interface is very intuitive."
  • "The data preparation capabilities need to be improved."

What is our primary use case?

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

What is most valuable?

The interface is very intuitive.

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

What needs improvement?

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.

For how long have I used the solution?

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

What do I think about the stability of the solution?

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

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

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

How was the initial setup?

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

What other advice do I have?

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.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Veronica Lambrechts - PeerSpot reviewer
Senior Manager - Data & Analytics at a tech services company with 201-500 employees
Real User
Easy to set up and the AutoML feature is helpful, albeit somewhat basic and should be enhanced
Pros and Cons
  • "The AutoML is helpful when you're starting to explore the problem that you're trying to solve."
  • "The AutoML feature is very basic and they should improve it by using a more robust algorithm."

What is our primary use case?

My primary use is for machine learning applications.

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

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.

What do I think about the stability of the solution?

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.

What do I think about the scalability of the solution?

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

How are customer service and technical support?

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

How was the initial setup?

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

What about the implementation team?

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

Which other solutions did I evaluate?

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.

What other advice do I have?

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.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
ChrisPeddie - PeerSpot reviewer
Tech Lead at a tech services company with 1,001-5,000 employees
Real User
Reduces work for our front-line agents, but the terminology for questions could be stronger
Pros and Cons
  • "The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses."
  • "Integration with social media would be a valuable enhancement."

What is our primary use case?

Our primary use for this solution is for customer service. Specifically, chat responses based on pre-defined questions and answers.

How has it helped my organization?

We have reduced the theme size front-line agents by ten percent using the AI elements on chat and email response.

What is most valuable?

The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses. This reduces our resources and costs.

The user interface that we have is relatively simple.

What needs improvement?

Some of the terminologies, or the way that the questions are asked, could be stronger. When people use local colloquialisms, it would be better if it understood rather than forwarding it to an agent.

If the frontline efficiencies were improved then we could pass this on to our clients.

Integration with social media would be a valuable enhancement.

For how long have I used the solution?

I have been using the Microsoft Azure Machine Learning Studio for about eighteen months.

What do I think about the stability of the solution?

The stability is good and we haven't had any issues.

What do I think about the scalability of the solution?

Scalability for us was fine.

We have about seven hundred users including customer service agents, sales agents, and cell phone account managers. It took us about twelve months to scale to this point, from an initial user base of seventy people, and we do not plan to increase usage further.

How are customer service and technical support?

We've got an internal IT department and we raised inquiries through them. They speak with whoever they need to in order to resolve the ticket.

Which solution did I use previously and why did I switch?

The previous solution that we were using was based on the Aspect platform. It was fifteen years old, which is why we reviewed it. We weren't able to offer any kind of AI or omnichannel experience using that platform, as its pure telephony. Anything else that we did was piecemeal. We switched because the platform couldn't offer the support that we needed for our clients.

How was the initial setup?

The initial setup is straightforward.

Our deployment took about six weeks, but that was also integrating the new telephony platform as well. For the AI elements, it was probably around five days.

Once the initial knowledge base was set it it took time to build and get it to where we needed it to be. Until that happens you can't really implement the AI element. This is what took about six weeks, so that it covered all of the inquiries that we wanted.

We started with an on-premises deployment and have moved to the cloud.

What about the implementation team?

We performed most of the implementation on-site by ourselves, but we had some help from a consultant to give us guidance.

What other advice do I have?

My advice to anybody who is implementing this solution is to be prepared to take a slow approach to get the best results.

The biggest lesson that I have learned from using this solution is that the strategic outsourcing contact will need to have a strategy for the next three to five years because the efficiencies that we will be gaining from AI will reduce the requirements on physical staff doing traditional roles. However, the support element will increase. It means that the roles will change and evolve over the next three to five years within the UK contact center based on the deployment of AI.

I think that we probably didn't start from the point that would have benefited us most in terms of the AI. Had we put more research into the front end then there would have been a lot less work during the implementation.

I would rate this solution a six out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Assistant Manager Data Literacy at K electric
Real User
You don't need to be a programmer to adopt this solution but the modeling feature needs improvement
Pros and Cons
  • "Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
  • "A problem that I encountered was that I had to pay for the 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."

What is most valuable?

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.                                                                                             

What needs improvement?

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.

For how long have I used the solution?

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

What do I think about the stability of the solution?

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.

How was the initial setup?

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

What other advice do I have?

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.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Big Data & Cloud Manager at a tech services company with 1,001-5,000 employees
Real User
Top 20
Stable and scalable with excellent technical support
Pros and Cons
  • "The solution is very fast and simple for a data science solution."
  • "The solution should be more customizable. There should be more algorithms."

What is our primary use case?

We primarily use the solution for data science.

What is most valuable?

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.

What needs improvement?

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

The solution needs more functionality.

For how long have I used the solution?

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

What do I think about the stability of the solution?

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

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

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

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

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.

What other advice do I have?

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.

Which deployment model are you using for this solution?

Public Cloud
Disclosure: My company has a business relationship with this vendor other than being a customer: partner
PeerSpot user
it_user1274883 - PeerSpot reviewer
CRM Consultant at a computer software company with 10,001+ employees
Vendor
Stable with good UI and machine learning capabilities
Pros and Cons
  • "The UI is very user-friendly and that AI is easy to use."
  • "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."

What is our primary use case?

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.

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

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

What do I think about the stability of the solution?

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.

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

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

Which solution did I use previously and why did I switch?

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

How was the initial setup?

The initial setup is very straightforward.

What about the implementation team?

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

What's my experience with pricing, setup cost, and licensing?

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

What other advice do I have?

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.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
PeerSpot user
Director at a tech services company with 1,001-5,000 employees
Real User
Easy to set up with good data normalization functionality
Pros and Cons
  • "The most valuable feature is data normalization."
  • "The data cleaning functionality is something that could be better and needs to be improved."

What is our primary use case?

Azure Machine Learning Studio works with our ERP solution.

What is most valuable?

The most valuable feature is data normalization.

What needs improvement?

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.

For how long have I used the solution?

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

What do I think about the stability of the solution?

This is a stable solution.

What do I think about the scalability of the solution?

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

How are customer service and technical support?

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

How was the initial setup?

The initial setup is straightforward and not too complex.

What about the implementation team?

We did the implementation by ourselves.

What's my experience with pricing, setup cost, and licensing?

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.

What other advice do I have?

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.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
PeerSpot user
it_user1050483 - PeerSpot reviewer
CEO at Inosense
Real User
Good support for Azure services in pipelines, but deploying outside of Azure is difficult
Pros and Cons
  • "The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
  • "If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."

What is our primary use case?

We used this solution for defining new predictive models, such as recommendation systems, but also price elasticity models for fraud detection, and the classification of customers.

We are not using this solution regularly. We are now using Azure Databricks.

What is most valuable?

The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure. You just have to drag and drop the services into your pipeline, and it can be applied through the pipeline. It's very helpful for data scientists. If you don't have any special knowledge in data science, just to know that you want to consume a service, that's all you need.

They have a tool for data gathering from some social networking sites such as Twitter and Facebook, which is great.

What needs improvement?

If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice.

One of the problems that we had was that you could only execute the model inside the machine learning environment. Comparing this to Databricks, if you create a pipeline, it could be in a notebook and you have all the code and then you can export your notebook to some other tool directly, for example in Jupyter and Spark. If you change tools then you won't lose your assets.

I would like to see improvements to make this solution more user-friendly.

They need to have some tools, like Apache Airflow, for helping to build workflows.

Better tools are needed to bring the data from existing storage into the environment where they can play with it and start to analyze what they already have, on-site. This is what the majority of people would like to do.

A feature that would be useful is to have some standard data transportation functions. They have ADF, Azure Data Factory, but it's a little bit heavy to manipulate. If they could have something more user-friendly, like Apache Airflow, it would be very nice.

For how long have I used the solution?

We have been using this solution for almost nine months.

What do I think about the stability of the solution?

This is a stable solution, although we have had problems with JavaScript. When you have many JavaScripts running, sometimes you have something that freezes, but we didn't know whether it was based on our network, the configuration, or the tools. It is difficult to identify the precise cause.

In general, there are no major issues.

What do I think about the scalability of the solution?

We never went into production because we switched to Azure Databricks. We did, however, try some performance testing and tried scaling some resources. The scalability of this solution is quite easy.

It is not difficult compared to some of the other tools that are available on Azure.

We have only five users including data engineers, data scientists, and one data DevOps engineer who was working with us on creating all of the DevOps pipelines for deploying all of our models.

How are customer service and technical support?

I have been in touch with technical support many times. The client I work for is a first-year client for them and we received some very useful support. The showed great willingness to help and they provided a lot of support for free.

We also had meetings with some experts on their data side and we had some free consultancy days given by Microsoft. It is called FastTrack and it is only available for some kinds of clients.

We are completely satisfied with the technical support.

Which solution did I use previously and why did I switch?

We did not use another solution prior to this one, but we now use Azure Databricks.

How was the initial setup?

The initial setup of this solution is straightforward.

The client site that we were working at had a proxy, and we were having a lot of trouble managing the rules inside the proxy because the Machine Learning Studio was not showing on the screen, in the browser, as it should. There are a lot of JavaScripts and this is a heavy client. There is a lot of feature logic performed on the client-side, such as the drag-and-drop. We had a lot of problems.

Besides that, once we fixed our network problem, it was straightforward.

What about the implementation team?

We implemented this solution on our own. The documents available on Microsoft Online made it quite easy.

What's my experience with pricing, setup cost, and licensing?

When we started using this solution, our licensing fees were approximately €1,000 (approximately $1,100 USD) monthly, but it was fluctuating. When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly. It was quite limited.

We expected the rate to be higher than this, at perhaps €10,000 (approximately $11,000 USD) per month, but it wasn't the case.

What other advice do I have?

Microsoft has increased the usability and the features since we first implemented this solution.

If I had to start this process over again, I would involve Microsoft earlier because they were great for providing support, as well as guidance on the architecture and what kind of stuff you can do with the tool, and what you should do with it. This was very helpful to orient the team to the right documentation and tutorials.

The second thing I would do is to start working with DevOps activity as soon as you can. We found ourselves redoing the same things many times, instead of having a DevOps pipeline to implement the stuff that we already stabilized, for example, and then not losing time.

The third thing is involving an integrator to help put together the big picture.

I would rate this solution a seven out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Rolf Lindgren - PeerSpot reviewer
CEO at a recruiting/HR firm with 1-10 employees
Real User
Visualizations are a key feature but it needs better operability with R

What is our primary use case?

Exploration of connections between biodata and psychometric test results.

What is most valuable?

Visualisation, and the possibility of sharing functions.

What needs improvement?

Operability with R could be improved.

For how long have I used the solution?

Less than one year.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
it_user848265 - PeerSpot reviewer
System Analyst at a financial services firm with 1,001-5,000 employees
Real User
Easy to deploy, drag and drop makes it easy to test various algorithms
Pros and Cons
  • "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."
  • "I would like to see modules to handle Deep Learning frameworks."

What is our primary use case?

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.

How has it helped my organization?

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.

What is most valuable?

  • 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.

What needs improvement?

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

For how long have I used the solution?

Less than one year.

What do I think about the stability of the solution?

No issues with stability.

What do I think about the scalability of the solution?

No issues with scalability.

How are customer service and technical support?

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

Which solution did I use previously and why did I switch?

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

How was the initial setup?

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

What's my experience with pricing, setup cost, and licensing?

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.

What other advice do I have?

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.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
it_user837534 - PeerSpot reviewer
Process Analyst
Real User
Split dataset, data visualization are helpful, but it needs integrated Pivot Table feature
Pros and Cons
  • "Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most."
  • "I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated."

What is our primary use case?

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.

How has it helped my organization?

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.

What is most valuable?

  • 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.

What needs improvement?

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.

For how long have I used the solution?

Less than one year.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
it_user833565 - PeerSpot reviewer
Software Engineer
Real User
Enables quick creation of models for PoC in predictive analysis, but needs better ensemble modeling
Pros and Cons
  • "MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse."
  • "The graphical nature of the output makes it very easy to create PowerPoint reports as well."
  • "Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
  • "Enable creating ensemble models easier, adding more machine learning algorithms."

What is our primary use case?

To create quick data analytic experiments, without incurring the time and cost of spinning up servers, setting up Hadoop, etc. 

Although MLS makes it very easy to deploy the resulting machine-learning models via REST API, I primarily use MLS as a means to quickly spin up experiments and create proof of concept models.

How has it helped my organization?

Not widely adopted at my old workplace, I only used this to create quick proofs of concept to try to convince management of the viability of a project.

What is most valuable?

MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse.

The easy drag and drop can create simple data science experiments. Low barrier to entry allows large number of candidates get started.

The graphical nature of the output makes it very easy to create PowerPoint reports as well.

What needs improvement?

Enable creating ensemble models easier, adding more machine learning algorithms.

For how long have I used the solution?

Less than one year.

What do I think about the stability of the solution?

Out of about 150-plus MLS experiments I have done, maybe two or three bugged out. Interestingly enough, those are the ones I can’t delete out of the account.

What do I think about the scalability of the solution?

Scalability, in terms of running experiments concurrently: Good. At max, I was able to run three different experiments concurrently.

Scalability in terms of deploying models: Unknown, I never deployed on Azure.  But I would guess REST API could probably easily handle a few K worth of hits per second, since that is how Microsoft is going to get paid.

How are customer service and technical support?

Never used it.

Which solution did I use previously and why did I switch?

The only other solution beyond this would be standard tools used by data scientists, like R, Python, etc. All of these would have a fairly high barrier to entry, requiring programming experience. The main selling point of MLS is the low barrier to entry, where even tech-savvy business people can use it.

How was the initial setup?

Simple. Create MLS live account (preferably paid ones), open MLS, done.

Caveat: Different organizations have different attitudes towards cloud use, especially with sensitive data. At Bridgestone, the hardest part was getting corporate approval to allow me to upload heavily treated, sensitive data to a cloud platform.

What's my experience with pricing, setup cost, and licensing?

To use MLS is fairly cheap. Even the paid account is something like $20/month,  unless you are provisioning large numbers of VMs for a Hadoop cluster.

The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API.

If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS.

Which other solutions did I evaluate?

R and Python.

Python + Pandas + scikit-learn: 

Pros: 

  • scikit-learn offers better performance for extremely large data sets
  • Large-data manipulation tools
  • Fairly good set of ML algorithms

Cons:

  • High barrier to entry, in terms of skill and knowledge
  • Fairly labor intensive to create large number of experiments

R + caret:

Pros:

  • Very good amount of ML algorithms (so many it may cause paralysis from too much choice, 200-plus algorithms)
  • Good performance, unless the data set is extremely large

Cons:

  • High barrier to entry
  • Data manipulation is a pain, you probably want to use another tool to pre-treat the data before loading it into R dataframes

What other advice do I have?

For data science professionals or programmers I would rate this solution a four out of 10. A major feature is missing: creating ensemble models. This can be achieved with the tool, but it's clumsy and slow.

For marketing or business professionals I would rate it an eight out of 10. It has a low barrier to entry, and can quickly create models that can be used for proof of concept and justify further investment in a full data science or Big Data project.

R and Python, in my mind, are still the way to go for a true data science/predictive analysis project. MLS's value is the ease of use and low barrier to entry. If one is not a programmer or statistician, MLS is a good way to get a project started, create a proof of concept.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Nitin-Jain - PeerSpot reviewer
Senior Associate - Data Science at a consultancy with 51-200 employees
Real User
​It has helped in reducing the time involved for coding using R and/or Python
Pros and Cons
  • "Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
  • "It helps in building customized models, which are easy for clients to use​.​​"
  • "​It has helped in reducing the time involved for coding using R and/or Python."
  • "​It could use to add some more features in data transformation, time series and the text analytics section."
  • "Microsoft should also include more examples and tutorials for using this product.​"

What is our primary use case?

I have used it to deploy predictive models in the healthcare sector.

How has it helped my organization?

It has helped in reducing the time involved for coding using R and/or Python. Also, web service is quite easy and convenient to use for clients. 

What is most valuable?

Its ability to publish a predictive model as a web based solution and integrate R and Python codes are amazing. It helps in building customized models, which are easy for clients to use.

What needs improvement?

It could use to add some more features in data transformation, time series and the text analytics section. Microsoft should also include more examples and tutorials for using this product.

For how long have I used the solution?

One to three years.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Jusiah Noah - PeerSpot reviewer
Co-Founder at a tech services company with 51-200 employees
Real User
Simplified development as scripts can be designed and implemented in real time

What is most valuable?

  • Feature-based selection
  • Compute
  • Data services.

How has it helped my organization?

Simplified development as scripts can be designed and implemented in real time.

What needs improvement?

I would like to see better prediction and analysis.

For how long have I used the solution?

We have used it for a few months.

What was my experience with deployment of the solution?

Good support is available when needed.

What do I think about the stability of the solution?

Stable at moment.

What do I think about the scalability of the solution?

There are no scalability issues at the moment as data volume is still low.

How are customer service and technical support?

Customer Service:

Customer service is good.

Technical Support:

Technical support is good.

Which solution did I use previously and why did I switch?

We did not use a solution previous to this one.

How was the initial setup?

It was complex to setup the workspace, but once it was done, we were good to go.

What about the implementation team?

We did the implementation in-house.

What was our ROI?

The ROI was 36%.

What's my experience with pricing, setup cost, and licensing?

The setup is a little complex, but it is worth it when it comes to security and efficiency.

Which other solutions did I evaluate?

We thought of doing this traditionally from scratch, but the Azure work space gives you the opportunity to utilize the environment and provide service in the shortest time possible.

What other advice do I have?

For the best, reliable results, it is the best solution to have in mind. Try it out.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Jusiah Noah - PeerSpot reviewer
Jusiah NoahCo-Founder at a tech services company with 51-200 employees
Real User

Scripts can be modified while the database is up and live running.Using the modify option in the the database
You can retrain model and choose a model out of the many models stored as a binary object in your database for feature use.
Ms sql server comes with support for R a statistical language that can do computations leaving you with only one worry optimizations.

Buyer's Guide
Download our free Microsoft Azure Machine Learning Studio Report and get advice and tips from experienced pros sharing their opinions.
Updated: November 2022
Buyer's Guide
Download our free Microsoft Azure Machine Learning Studio Report and get advice and tips from experienced pros sharing their opinions.