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Microsoft Azure Machine Learning Studio OverviewUNIXBusinessApplication

Microsoft Azure Machine Learning Studio is #1 ranked solution in top AI Development Platforms and #4 ranked solution in top Data Science Platforms. IT Central Station users give Microsoft Azure Machine Learning Studio an average rating of 8 out of 10. Microsoft Azure Machine Learning Studio is most commonly compared to Databricks: Microsoft Azure Machine Learning Studio vs Databricks. The top industry researching this solution is Computer Software Company, accounting for 25% of all views.
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 Studio is also known as Azure Machine Learning, MS Azure Machine Learning Studio.

Microsoft Azure Machine Learning Studio Buyer's Guide

Download the Microsoft Azure Machine Learning Studio Buyer's Guide including reviews and more. Updated: October 2021

Microsoft Azure Machine Learning Studio Customers

Walgreens Boots Alliance, Schneider Electric, BP

Microsoft Azure Machine Learning Studio Video

Pricing Advice

What users are saying about Microsoft Azure Machine Learning Studio pricing:
  • "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."
  • "The licensing cost is very cheap. It's less than $50 a month."

Microsoft Azure Machine Learning Studio Reviews

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Alexandre Akrour
CEO at Inosense
Real User
Top 5Leaderboard
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.
CS
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. 

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 technical 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.
Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: October 2021.
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ChrisPeddie
Tech Lead at a tech services company with 1,001-5,000 employees
Real User
Top 10
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.
SD
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
EC
student at a university with 201-500 employees
Real User
Top 20
Stable, with a straightforward setup and is very easy to use

Pros and Cons

  • "The initial setup is very simple and straightforward."
  • "It would be nice if the product offered more accessibility in general."

What is our primary use case?

I use the solution for learning purposes for the most part.

How has it helped my organization?

Personally, I got interested in data science and machine learning due to using this product. After some time with it, these topics didn't intimidate me.

What is most valuable?

The solution is very easy to use. It's user-friendly and simple to navigate.

The initial setup is very simple and straightforward.

The solution is quite stable.

What needs improvement?

It's the first software that I've used in terms of machine learning. Therefore, I don't have anything to compare it to, however, it was okay for me. I didn't have any problems or anything.

Maybe it can be integrated with something else. For example, business analytics. That way, you could also give creative reports. It's possible it could be integrated with the Power BI, as it's also Microsoft. That said, I'm not really sure. It if isn't possible, it's something they could consider for a future release.

Microsoft needs to be sure to monitor the security and ensure they are constantly updating it.

It would be nice if the product offered more accessibility in general.

For how long have I used the solution?

I've only been using the solution for a short amount of time. It's just for a course at school.

What do I think about the stability of the solution?

The solution is stable. I didn't have any lags or anything. It was smooth. There are no bugs or glitches. I don't recall it crashing or freezing on me.

What do I think about the scalability of the solution?

The solution seems to be able to work well for companies both large and small. However, I did not personally attempt to scale it.

How are customer service and technical support?

I never really dealt with technical support directly. I have my teacher to teach or to ask questions to. He would often recommend these online tutorials to learn about the solution as well. I never really thought of asking a chat box, for example, of Microsoft, where I could type any help. I never really considered it. Therefore, I can't speak to how helpful or responsive they typically are.

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

I did not use a different solution. This is the first solution I used for machine learning.

How was the initial setup?

The initial setup was not difficult or overly complex. It's very straightforward, very simple, and very easy to understand. 

Everything is just written down in a way that was an easy way to understand, even for someone who isn't used to the packages of Microsoft.

What about the implementation team?

I handled the deployment myself. I did not need the help of a consultant or integrator.

What other advice do I have?

I'm just a student. I was learning about machine learning via this product.

I'm not sure which deployment model we are using.

I would rate the solution at an eight out of ten.

I would advise other potential users to just start using it. If they really want to learn it, it will take a bit of time. Even though it's easy to use, you need some knowledge in data science. That will help make the process easier.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
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MD
Head Of Analytics Platforms and Architecture at a consumer goods company with 10,001+ employees
Real User
Top 10
Stable, easy to use, and quick to implement

Pros and Cons

  • "The solution is very easy to use, so far as our data scientists are concerned."
  • "There should be data access security, a role level security. Right now, they don't offer this."

What is our primary use case?

We primarily use this product for its price elasticity and the product mix on offer.

What is most valuable?

The solution is very easy to use, so far as our data scientists are concerned. 

There's an excellent self-developing capability that is provided that makes the product unique.

The solution is very stable. We haven't had any issues with its performance thus far.

We've found that, if you need to, you can scale the product.

The solution is very quick to implement.

What needs improvement?

We've found that the solution runs at a high cost. It's not cheap to utilize it.

Two additional items I would like to see added in future versions are software life cycle features and more security capabilities. There should be data access security, a role level security. Right now, they don't offer this.

For how long have I used the solution?

I've only really been using the solution for the last few months. It really hasn't been too long at this point in time.

What do I think about the stability of the solution?

The solution is reliable. There are no bugs or glitches. We haven't experienced crashes or freezing. It's stable. It's very good in that sense.

What do I think about the scalability of the solution?

If a company needs to scale the solution, they should have no problem doing so. I don't see any aspect of the solution that would stop a user from expanding it as needed.

Currently, we only have a handful of users. There are only about five to seven people on the product right now.

We do plan to continue to use the product and to increase usage in the future.

How are customer service and technical support?

We've dealt with technical support in the past. We do, from time to time, have issues, which we work with the Microsoft team to resolve.

Overall, we've been satisfied with the level of support they have provided us.

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

We did not previously use a different product. This is the first type of solution that we've used.

How was the initial setup?

The initial setup is quick and easy. It's not complex at all. There is no installation per se. It's simply that you plug into the cloud and start using it.

For deployment, you likely need a two or three-member team. You don't need a lot of people to get it up and running. Largely they are just managers, admins or engineers, or a combination of those three.

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

The solution is quite expensive. It's something the organization should work on improving.

We use this product on a pay-per-use basis, Therefore, there is no licensing fee. It's embedded in the cost of using the Studio.

What other advice do I have?

We're just a Microsoft customer. We don't have a business relationship with Microsoft.

Currently, it is my understanding that we are using the latest version of the solution.

I'd recommend this product to other organizations.

Overall, I would rate the solution at an eight out of ten.

Which deployment model are you using for this solution?

Public Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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Ariful Mondal
Consulting Practice Partner - Data, Analytics & Artificial Intelligence at Wipro Ltd
Real User
Top 5Leaderboard
User-friendly, no code development, and good pricing but they should offer an on-premises version

Pros and Cons

  • "It's good for citizen data scientists, but also, other people can use Python or .NET code."
  • "They should have a desktop version to work on the platform."

What is our primary use case?

We plan to use this solution for everything in business analytics including data harmonization, text analytics, marketing, credit scoring, risk analytics, and portfolio management.

How has it helped my organization?

It allows us to do machine learning experiments quickly.

We did not have machine learning solutions or platform earlier.

What is most valuable?

It's user-friendly, and it's a no-code model development. It's good for citizen data scientists, but also, other people can use Python, R or .NET code.

If you are on Microsoft Cloud, the development and implementation are super easy.

What needs improvement?

Every tool requires some improvement. They have already improved many things. They had added new features and a new pipeline.

They should have an on-premise version, other than Python and R Studio, which is only good for cloud-based deployments.

If they could have a copy of the on-premise version on Mac or Linux or Windows, it would be helpful.

It should have the flexibility to work o the desktop. They should have a desktop version to work on the platform.

For how long have I used the solution?

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

What do I think about the stability of the solution?

It's a stable solution. Microsoft is very stable in general.

What do I think about the scalability of the solution?

It's very scalable because it is using Microsoft cloud compute power.

We want to extend organization-wide, but currently, we are only working on a use case basis.

How are customer service and technical support?

We have not required help from technical support, but Microsoft technical support comes with it when you subscribe.

How was the initial setup?

Deployment of the tool is simple. Just one click on Microsoft. Once you have procured the license, you can just log in and use it. It's a ready-to-use tool.

When you deploy the solution after analytic development, it depends on the project but it can take anywhere from one month to six months.

Also, depending on the infrastructure, the initial deployment can take one week to a month.

What about the implementation team?

In-house expertise.

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

The licensing cost is very cheap. It's less than $50 a month would costs for multiple users.

What other advice do I have?

If you want to build a solution quickly without knowing any coding, it's pretty good to start with.

I will take a week to learn, from my experience. For anyone who is interested in trying it, they should start with the free version, which is free for up to 10 gigabytes of workspace.

Just log in and start developing and exploring the tool before onboarding.

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

Which deployment model are you using for this solution?

Public Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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MikayilBekooglu
CRM Consultant at a computer software company with 10,001+ employees
Vendor
Top 5
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
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