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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 #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 16% 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: September 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

Microsoft Azure Machine Learning Studio Pricing Advice

What users are saying about Microsoft Azure Machine Learning Studio pricing:
  • "In terms of pricing, for any cloud solution, you should know the tricks of the trade and how to use it, otherwise, you'll end up paying a lot of money irrespective of the cloud provider, so at least for Microsoft Azure Machine Learning Studio pricing versus AWS, I would rate it three out of five, with one being the most expensive, and five being the cheapest. It could be cheaper, but you also have to be careful when choosing the plans, for example, consider the architecture and a lot of other factors before choosing your plan, if you don't want to end up paying more. If your cloud provider has an optimizer that seems to be available in every provider, that would keep alerting you in terms of resources not being used as much, then that would help you with budgeting."
  • "My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it."
  • "The licensing cost is very cheap. It's less than $50 a month."
  • Microsoft Azure Machine Learning Studio Reviews

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    N Kumar - PeerSpot reviewer
    Associate Director Of Technology at a tech vendor with 10,001+ employees
    MSP
    Top 20
    Has a drag and drop feature and easier learning curve, but the number of algorithms available could still be improved
    Pros and Cons
    • "In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."
    • "As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly. When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft. I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine. What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased."

    What is most valuable?

    In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio.

    I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so  this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning.

    What needs improvement?

    As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly.

    When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft.

    I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine.

    What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased.

    For how long have I used the solution?

    I've been working with Microsoft Azure Machine Learning Studio for nearly two years now.

    What do I think about the stability of the solution?

    Microsoft Azure Machine Learning Studio is a stable solution. My company is already using it in production. At least customers use the recommendations from Microsoft Azure Machine Learning Studio in production, so the solution is quite stable, at least in cases developed by my company.

    Buyer's Guide
    Microsoft Azure Machine Learning Studio
    September 2022
    Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: September 2022.
    635,987 professionals have used our research since 2012.

    What do I think about the scalability of the solution?

    Microsoft Azure Machine Learning Studio is a solution that's easy to scale. It's pretty easy because it is hosted on Kubernetes, and there is an option in the portal where I can simply move my plan from standard to enterprise. The solution also has an automatic scaling option available because it is on Kubernetes, so it can scale automatically. I'm seeing that it's quite scalable. This has nothing to do with availability because it just runs in the background, and it is not customer-facing, but the output is customer-facing, so availability is a different case, but in terms of scalability, Microsoft Azure Machine Learning Studio is scalable.

    How are customer service and support?

    The technical support team for Microsoft Azure Machine Learning Studio was pretty good, though I had to tailor the answers to my requirement, but would rate support a four out of five. Most of the questions my company had, more or less, the support team already experienced, so the team had answers readily available which means there wasn't a need to do a lot of R&D, so getting answers from technical support didn't take a lot of time.

    How was the initial setup?

    In terms of setting up Microsoft Azure Machine Learning Studio, initially, when my company started, the documentation wasn't so good, but now it has improved. Provisioning the solution only takes a few clicks, so it's no big deal, but setting up the pipelines because no enterprise will have a single environment, you'll have to create multiple pre-production and end production environments, so moving my latest changes to the next environment was a bit of a challenge.

    Many terminologies are now in the market such as DevSecOps, and MLOps, so that MLOps documentation was available initially, but it wasn't very explanatory, but now, there's a lot of improvement in the MLOps documentation and that will help me move and propagate my changes from one environment to another.

    Microsoft has made improvements into the tutorials, especially on MLOps. Finding MLOps experts in the market was also very tough initially, so my company was trying to learn on the job and do it, so it took some thinking and time, but it's still good because you can learn on the job and do it, but you won't always have the luxury of time to learn it.

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

    In terms of pricing, for any cloud solution, you should know the tricks of the trade and how to use it, otherwise, you'll end up paying a lot of money irrespective of the cloud provider, so at least for Microsoft Azure Machine Learning Studio pricing versus AWS, I would rate it three out of five, with one being the most expensive, and five being the cheapest. It could be cheaper, but you also have to be careful when choosing the plans, for example, consider the architecture and a lot of other factors before choosing your plan, if you don't want to end up paying more. If your cloud provider has an optimizer that seems to be available in every provider, that would keep alerting you in terms of resources not being used as much, then that would help you with budgeting.

    Which other solutions did I evaluate?

    We evaluated quite a lot of options. We compared Microsoft Azure Machine Learning Studio against Google Cloud and AWS solutions, and there were several others available in the market. I'm trying to recollect the names which we compared the solution with. We did the benchmarking, but we went with Microsoft Azure Machine Learning Studio because our clients and their data were on Azure, though that doesn't necessarily make you go with the solution. After all, you can pull the data from any other cloud as well. For our use case, however, we found many of the things were readily available and the learning curve for Microsoft Azure Machine Learning Studio compared to others was better and easier. We didn't have to search for experts in the market to hire them because we could have our in-house team learn and deliver the solution on the job.

    What other advice do I have?

    Microsoft Azure Machine Learning Studio is a cloud-native solution. It's completely cloud-based.

    My company has eight users of Microsoft Azure Machine Learning Studio.

    My rating for Microsoft Azure Machine Learning Studio is seven 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
    Flag as inappropriate
    PeerSpot user
    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.
    PeerSpot user
    Buyer's Guide
    Microsoft Azure Machine Learning Studio
    September 2022
    Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: September 2022.
    635,987 professionals have used our research since 2012.
    Mahendra Prajapati - PeerSpot reviewer
    Senior Data Analytics at a media company with 1,001-5,000 employees
    Real User
    Top 10
    Creates more accurate models and is easy to use even for users who don't know much about coding because of its drag-and-drop feature
    Pros and Cons
    • "What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it."
    • "Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners."

    What is our primary use case?

    In terms of use case, we implement Microsoft Azure Machine Learning Studio using Python libraries, so basically, we have a centralized studio where we just have to drag and drop features and create the model out of the data that we have. Microsoft Azure Machine Learning Studio is pretty easy to use even for people who don't know much about coding. They just need to know the features and libraries, so it's similar to Tableau and Alteryx because users can drag and drop features to create models or pipelines. We create and deploy pipelines through Microsoft Azure Machine Learning Studio.

    What is most valuable?

    What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use.

    Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it.

    What needs improvement?

    Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it.

    What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners.

    For how long have I used the solution?

    I've used Microsoft Azure Machine Learning Studio in the past year in my previous company, though I'm unsure about which version I was using at the time.

    What do I think about the stability of the solution?

    The functionality of Microsoft Azure Machine Learning Studio, specifically its underlying computing power, was managed by Azure, so stability-wise, it's a good solution.

    What do I think about the scalability of the solution?

    Microsoft Azure Machine Learning Studio is a scalable tool. My previous company was on a volume-based model with it, and even if the data is large, it's easy to scale.

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

    The company decided to go with Microsoft Azure Machine Learning Studio because of the partnership with Azure Cloud, so it's a way to leverage all features. The data was also hosted on the Azure platform, which made it pretty straightforward to use Microsoft Azure Machine Learning Studio rather than integrate with other tools.

    How was the initial setup?

    Setting up Microsoft Azure Machine Learning Studio was very easy and is comparable to how easy it is to use any feature available in the tool.

    Configuring the pipeline takes just ten to fifteen minutes, but that would still depend on the data volume.

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

    My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it.

    What other advice do I have?

    Approximately two hundred to three hundred people, mostly part of the data analytics team, were using Microsoft Azure Machine Learning Studio within the company.

    My advice to anyone using Microsoft Azure Machine Learning Studio for the first time is to have an understanding of machine learning, deep learning, and libraries. You should also know the scripts because features are created on top of the machine learning libraries used in Python. If you want more optimizations or a better accuracy rate, you need a proper understanding of machine learning or a machine learning background before using Microsoft Azure Machine Learning Studio.

    I'm rating Microsoft Azure Machine Learning Studio eight out of ten because it still needs some improvement. For example, because the drag-and-drop feature of the tool was written or based on Python, when you're creating a model in Microsoft Azure Machine Learning Studio, you'll get good accuracy by writing the script in Python, so accuracy isn't standard. You can customize your metrics to get good accuracy, but what you'll get is completely generalized, so whatever use case you feed into the pipeline, it'll create a test to get good accuracy, but it'll not give you a guarantee that this will be the only accuracy you'll get.

    The previous company I worked in was a partner of Microsoft Azure Machine Learning Studio.

    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
    Flag as inappropriate
    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
    student at a university with 201-500 employees
    Real User
    Top 10
    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.
    PeerSpot user
    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.
    PeerSpot user
    Ariful Mondal - PeerSpot reviewer
    Consulting Practice Partner - Data, Analytics & AI at FH
    Real User
    ExpertModerator
    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.
    PeerSpot user
    Rishi Verma - PeerSpot reviewer
    Practice Director at Birlasoft IndiaLtd.
    Real User
    Top 10
    Enables quick development of solutions, particularly those that are text analytics and cognitive-based
    Pros and Cons
    • "Auto email and studio are great features."
    • "Using the solution requires some specific learning which can take some time."

    What is our primary use case?

    The use cases of this product are primarily for the BFSI; digitization and building machine learning models that provide recommendations for creating analytical insights from extracted data. We also do Jupyter Notebook authoring. We are partners with Microsoft and I'm a practice director.  

    How has it helped my organization?

    The product enables quick data preparation and data processing pipeline as well as modeling work and it's all part of Azure Machine Learning. It also gives us an idea of what machine learning model is good to use because the hyperparameter tuning is done automatically which saves us time and effort. 

    What is most valuable?

    Auto email and the studio are great features. 

    What needs improvement?

    It's not that easy to master the program, it requires some specific learning. If we want to extend the program to include inexperienced users, it can take some time for them to learn the solution. It would be nice if they added GPU solutions. Most of the solutions coming out now are video analytics or edge computing-based and Azure should have that focus.  

    What do I think about the stability of the solution?

    We haven't had any issues with stability. 

    What do I think about the scalability of the solution?

    We haven't faced any challenges with scalability. If there are any issues, our Microsoft infract team pitches in but we haven't had any serious problems. We have around 25 to 30 customers accessing this solution. Maintenance is straightforward and doesn't require more than one person. 

    How are customer service and support?

    Customer support is very good, they are prompt and helpful in solving problems. 

    How would you rate customer service and support?

    Positive

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

    Our switch to AMLS was an organic development that came from the needs of our customers and was based on the quick time to develop and the pre-built machine learning models that the solution has.

    How was the initial setup?

    The initial setup is straightforward with deployment time depending on the environment. It depends on how many machine learning models we need to develop, the type of resources, the different sources, data volumes, etc. 

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

    We don't deal with licensing, that is something our customers are responsible for.  My understanding is that the cost is $50 for the digitization of 1,000 pages. I think it should be reduced to somewhere between $20 to $30 per 1,000 pages so that we can make a better offer to our customers. 

    What other advice do I have?

    I believe Azure Machine Learning has a very good pre-built model which enables quick development of solutions, particularly text analytics and cognitive-based solutions. 

    I rate this solution nine out of 10. 

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