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Vijay Rameshkumar - PeerSpot reviewer
Data Scientist at Sunergy
Real User
Top 20
Empowers developers to build, deploy, and manage high-quality models faster
Pros and Cons
    • "In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."

    What is our primary use case?

    I've had experience working for two distinct companies. My previous employer operated in the telecom domain, primarily focused on telecom-related projects. In my current role, which is in the shipping domain, we primarily manage shipping cases. Furthermore, our current work predominantly revolves around a machine learning platform implemented on storage systems.

    How has it helped my organization?


    Performing tasks in a cloud service is incredibly straightforward. It offers excellent scalability and provides both JUPITAN and designer environments. There's no need to write extensive code; instead, you can simply drag and drop elements and connect components effortlessly. This allows for the creation of end-to-end workflows with minimal effort. It's a user-friendly and scalable solution, which is why I prefer working with it. Additionally, it allows for effective version control management.

    What needs improvement?

    In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally.

    One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform.

    For how long have I used the solution?

    I have been working with Microsoft Azure Machine Learning Studio for three years. 

    Buyer's Guide
    Microsoft Azure Machine Learning Studio
    June 2025
    Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: June 2025.
    861,390 professionals have used our research since 2012.

    What do I think about the stability of the solution?

    I would rate it eight out of ten. 

    What do I think about the scalability of the solution?

    I would rate it eight out of ten.

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

    Yes, I have worked with Azure in my previous experiences.

    How was the initial setup?


    The initial setup duration largely depends on your prior experience with the service. While setting up is generally straightforward, the time-consuming part comes in when you have to repeatedly input your username and password to connect with different building blocks. The deployment time can vary significantly. If you opt for an internal deployment suggested by Azure, it's relatively quick. However, if you're looking for an external deployment, it might take more time. The deployment timeline hinges on the project's scope and architecture. 
    Based on my experience, I find that it typically doesn't require a substantial amount of time.

    In my previous experience using Azure and Machine Learning Studio, the database service offers an integrated option for data cleaning and ETL. This means you don't need to allocate extra time for data preparation and deployment because everything is interconnected. Monitoring progress is also feasible. Therefore, in terms of deployment and data engineering, there's generally not a significant increase in time required unless the project scope is extensive. For moderately scaled projects, a single person can handle the entire deployment.

     The initial setup is moderate and I would rate it seven out of ten.

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

    There isn’t any such expensive costs and only a standard license is required. 

    What other advice do I have?


    It is a good solution and will prove to be very helpful for your project. I would recommend it and rate it seven out of ten.

    Which deployment model are you using for this solution?

    Private Cloud
    Disclosure: My company does not have a business relationship with this vendor other than being a customer.
    PeerSpot user
    SezaDursun - PeerSpot reviewer
    Senior Expert Data Science Product Owner at Boyner Buyuk Magazacilik A.S.
    Real User
    Top 20
    Helps users develop ML algorithms easily
    Pros and Cons
    • "The product's initial setup phase is easy."
    • "The high price of the product is an area of concern where improvements are required."

    What is our primary use case?

    I use the solution in my company for some product prediction purposes and in the retail processes. My company collects data in Azure and Cynet. My company develops some ML algorithms in Microsoft Azure Machine Learning Studio. Two years ago, I had a training session with Microsoft, so now I can develop ML algorithms in the retail sector.

    What needs improvement?

    The high price of the product is an area of concern where improvements are required. The product needs to be available at a cheaper price. I think the tool needs to be made available to children for free, especially if you have a student's or a teacher's email ID from a university.

    For how long have I used the solution?

    I have been using Microsoft Azure Machine Learning Studio for two years. I am a user of the solution.

    What do I think about the stability of the solution?

    It is a stable solution. I have seen a lot of products, but I feel that Microsoft Azure Machine Learning Studio is the most stable tool in the market. Stability-wise, I rate the solution an eight out of ten.

    What do I think about the scalability of the solution?

    It is a scalable solution. Scalability-wise, I rate the solution an eight out of ten.

    For development purposes, there are ten people, and since my institute expanded its use, I think it will increase to fifty.

    I plan to increase the tool's usage.

    How are customer service and support?

    I got support for the tool in Turkey with the help of Mircorsoft's admins and consultants and they were okay. I rate the technical support an eight out of ten.

    How would you rate customer service and support?

    Positive

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

    I evaluated DataIQ against Microsoft Azure Machine Learning Studio. Most of the applications I used were small or niche tools, while Microsoft Azure Machine Learning is huge and expandable.

    How was the initial setup?

    The product's initial setup phase is easy. I rate the product's initial setup phase as an eight out of ten.

    The solution is deployed using Azure's cloud services.

    The solution time for deployment can take a few hours. I work in an enterprise with a medium range of data.

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

    I rate the product price as a nine on a scale of one to ten, where ten means it is very expensive.

    What other advice do I have?

    You can take care of the input of the datasets, and then Microsoft Azure Machine Learning Studio will provide everything for you. The tool has drag-and-drop options, and it becomes easy to develop anything, acting as a time-saver.

    The drag-and-drop options in Microsoft Azure Machine Learning Studio influence the machine learning workflow as it allows you to put in some links, and it integrates with nearly all of your systems. You can use the datasets with the tool program to see the features and dimensions of all the datasets. Then you can choose some ML algorithm about the datasets, and it recommends the program, after which you can test a lot of models, but normally, you can't do it because it's not in Python or something else, leaving you confused. In Microsoft Azure Machine Learning Studio, there is a system from beginning to end that covers everything one should do.

    The tool does have an influence on performance as it makes things faster and more productive.

    I can recommend the tool to my colleagues. It provides the developer or product owner the ability to watch the system overall. If you use the tool you can begin learning about ML and you become more effective and faster which is important for enterprise.

    I rate the tool a nine out of ten.

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

    Microsoft Azure
    Disclosure: My company does not have a business relationship with this vendor other than being a customer.
    PeerSpot user
    Buyer's Guide
    Microsoft Azure Machine Learning Studio
    June 2025
    Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: June 2025.
    861,390 professionals have used our research since 2012.
    Data Product Owner at World Media Group, LLC
    Real User
    Top 20
    Easy to use, increases productivity, and allows users to quickly build and experiment with machine learning models
    Pros and Cons
    • "The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
    • "One area where Azure Machine Learning Studio could improve is its user interface structure."

    What is our primary use case?

    Our use cases  involve customer segmentation for targeted marketing, where I use machine learning to identify potential customers interested in a new product. Another is a recommendation system on our company website, where I use machine learning to suggest additional products to customers based on their browsing or purchase history. Lastly, there is pricing estimation, where I use machine learning to predict the price of an item or article.

    What is most valuable?

    The features of Azure Machine Learning Studio that I find most valuable depend on the type of model I'm working with. For integration, knowing halfway indicators is crucial to assess model performance. For classification models, the confusion matrix is important for evaluation, while for regression models, statistical tests like the provision statistics are valuable.

    What needs improvement?

    One area where Azure Machine Learning Studio could improve is its user interface structure. Simplifying the initial information presented upon first use could make it more accessible, especially for users with limited technical skills. Providing only essential information upfront would enhance the user experience and reduce complexity.

    For how long have I used the solution?

    I have been working with Microsoft Azure Machine Learning Studio for three years.

    What do I think about the stability of the solution?

    I would rate the stability of the solution at a six out of ten. Improving stability involves finding people with the right skills to handle problems that arise. While stability depends on how well the solution is installed, ongoing efforts are needed to address issues and refine the system. We are working step by step to identify and solve problems, but there's room to find more comprehensive solutions as they come up.

    What do I think about the scalability of the solution?

    I would rate the scalability of Azure Machine Learning Studio at about a seven out of ten. While it offers high scalability, it can be challenging for less technical users and may encounter issues with defects and industrial licensing, particularly in logistics projects.

    At our company, we use Azure Machine Learning Studio daily.

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

    Before Microsoft Azure Machine Learning Studio, we used on-premises solutions. We made the switch to Azure Machine Learning and the cloud to modernize our projects and leverage the benefits of cloud computing.

    What other advice do I have?

    I use Azure Machine Learning Studio for predictive modeling in my project. I follow a workflow that involves selecting data, preprocessing it, training models, and deploying them. The Studio's tools cover all these steps, making it convenient for me to build and deploy predictive models.

    In a specific scenario, I used Azure Machine Learning Studio for data preprocessing by creating new variables. This involved tasks like transforming variable types or combining multiple variables to create new ones. Additionally, I employed cross-validation techniques, such as k-fold validation, to assess model performance and select appropriate metrics for evaluation.

    The most important aspect of my machine learning projects is the quality of the data. It is crucial to determine whether the data can provide meaningful information relevant to the project's use case, regardless of the specific tools or features used.

    The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow. It is easy to use and increases productivity by allowing quick experimentation and visualization of data pipelines. This feature enables me to iterate rapidly and efficiently, especially for small projects or presentations.

    I would rate the performance of the solution at an eight out of ten for my team. However, our data volume is not the largest. While I believe our performance is strong, other companies might rate it lower due to different circumstances.

    My advice for someone considering installing Azure Machine Learning Studio is that it is user-friendly, especially for technical users. You can easily upload data and analyze it with the examples provided. The drag-and-drop interface makes it intuitive, and upgrading to this tool for data analysis is a good idea.

    Overall, I would rate Azure Machine Learning Studio as 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: My company does not have a business relationship with this vendor other than being a customer.
    PeerSpot user
    HéctorGiorgiutti - PeerSpot reviewer
    Senior Machine Learning Engineer at EY
    Real User
    Requires minimal maintenance, is scalable, and stable
    Pros and Cons
    • "The solution is really scalable."
    • "The price of the solution has room for improvement."

    What is our primary use case?

    I usually order a machine for training my models. I build the machine myself to include various images for working with Python or IR. I upload my data and scripts to the cloud and run the training process.

    What is most valuable?

    I'm beginning to learn about Databricks, which is a framework that works on Azure, AWS, and GCP. It has more power than the Azure main infrastructure, so I'm starting to explore it for things such as training models. I like all the features that Azure's main infrastructure provides, so I don't have a preferred feature. I think many people will move to Azure Databricks in the future.

    What needs improvement?

    The price of the solution has room for improvement.

    For how long have I used the solution?

    I have been using the solution for almost three years.

    What do I think about the stability of the solution?

    I don't have much experience with production environments since they are usually managed by DevOps rather than me when I deploy my work. However, I believe the solution is stable.

    What do I think about the scalability of the solution?

    The solution is really scalable.

    How are customer service and support?

    Microsoft has great technical support, which is really beneficial.

    How was the initial setup?

    The initial setup depends on the developer's knowledge of machine learning models as to whether it is easy or difficult. With a good understanding of these models, then we can get to work quickly in the environment. With 20 years of experience in IT, making applications on legacy platforms and non-web platforms, I have found that Azure has a very good implementation. The platform is so comprehensive that it doesn't matter what kind of work we do, we can find the tools needed to get the job done. In comparison to what I was doing five years ago, Azure is a great platform and I really enjoy working with it.

    We should allocate up to 12 percent of our project time to deployment. Depending on the complexity of the solution, we should expect to spend more time on deployment.

    What about the implementation team?

    Some of our implementations are in-house and others are not. 

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

    The solution cost is high.

    What other advice do I have?

    I give the solution an eight out of ten.

    I began using Azure three months ago, connecting my local Visual Code environment with the actual environment. This was a major improvement for me, as I can now work and run experiments on my local computer. I'm really pleased with how comfortable I am using Azure on all platforms.

    The solution requires a minimum of one developer for maintenance. We need a DevOps developer and the tech lead to define the scope of the problems to be solved. The tech lead will provide guidance and oversight, while the DevOps developer will be responsible for implementing the solutions.

    I enjoy working and have no difficulty in recommending Azure Machine Learning Studio to others, however, I recognize that there are many implementations utilizing AWS. AWS is a formidable competitor, so it is essential to be familiar with both solutions. Unfortunately, I have missed out on opportunities because I am not situated in the US. The environment is excellent, however, the large American market and the companies therein rely heavily on our work. This requires me to stay apprised of current developments, such as the widespread adoption of AWS, and learn how to use alternative platforms.

    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 does not have a business relationship with this vendor other than being a customer.
    PeerSpot user
    Gerald Dunn - PeerSpot reviewer
    Director and Owner at Standswell Ltd
    Real User
    Top 10
    Provides a range of tools and libraries we can access
    Pros and Cons
    • "The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant."
    • "It would be great if the solution integrated Microsoft Copilot, its AI helper."

    What is our primary use case?

    We use Microsoft Azure Machine Learning Studio to generate predictive sales analytics and determine customer behavior.

    How has it helped my organization?

    Through the solution's customer data analysis, we conduct customer data experiments, test hypotheses, and develop sales strategies.

    What is most valuable?

    The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant. The solution's data pipelines are easier to configure, and the solution provides a range of tools and libraries we can access.

    What needs improvement?

    It would be great if the solution integrated Microsoft Copilot, its AI helper.

    For how long have I used the solution?

    I have been using Microsoft Azure Machine Learning Studio for one year.

    What do I think about the stability of the solution?

    The solution's stability depends on the fragility of libraries and the availability of services. Sometimes, the demand is very high in the public cloud, and performance and availability issues have occurred.

    I rate the solution a six out of ten for stability.

    What do I think about the scalability of the solution?

    Microsoft Azure Machine Learning Studio is a very scalable solution. Three people are using the solution in our organization.

    I rate the solution an eight out of ten for scalability.

    How was the initial setup?

    I rate the solution a seven out of ten for the ease of its initial setup.

    What about the implementation team?

    The solution’s deployment takes one hour.

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

    There is a lack of certainty with the solution's pricing. The risk is the pricing is high without you necessarily knowing. The workload drives the solution's pricing. If you give it a lot to do, it will cost a lot of money. It's about committing to how much you want to pay for. You don't necessarily know what you'll get for the price level that you agree.

    On a scale from one to ten, where one is cheap and ten is expensive, I rate the solution's pricing a seven out of ten.

    Which other solutions did I evaluate?

    Before choosing the solution, we evaluated Databricks. We chose Microsoft Azure Machine Learning Studio to get as close to the Microsoft pattern as possible. We have a Microsoft first policy, and therefore, unless there's a reason not to use Microsoft, we choose Microsoft.

    What other advice do I have?

    I would recommend Microsoft Azure Machine Learning Studio to other users. I would also ask users to compare the solution with Microsoft Fabric, which is a collection of components to put a workflow together end to end.

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

    Which deployment model are you using for this solution?

    Public Cloud
    Disclosure: My company does not have a business relationship with this vendor other than being a customer.
    PeerSpot user
    N Kumar - PeerSpot reviewer
    Associate Director Of Technology at a tech vendor with 10,001+ employees
    MSP
    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.

    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
    PeerSpot user
    Rishi Verma - PeerSpot reviewer
    Practice Director at Birlasoft IndiaLtd.
    Real User
    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
    PeerSpot user
    Dimitris Iracleous - PeerSpot reviewer
    Lead Technical Instructor at Code.Hub
    Real User
    Top 5Leaderboard
    A well organized solution that helps to create pipelines in minutes
    Pros and Cons
    • "The product is well organized. The thing is how we will get the models to work within our code. We have some suggestions there, but we want to gain more experience and be ready to answer that because we are currently working on this and don't have all the answers yet. The tool is well organized. What I am very happy about is the ease of deploying new resources. You can easily create your pipeline within minutes."
    • "One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. The tool should keep on updating new algorithms and not stay static."

    What is our primary use case?

    We have data from our business, and we want to make AI models. The question is how we want to use those models in our business. That's what we're going to do next year.

    What is most valuable?

    The product is well organized. The thing is how we will get the models to work within our code. We have some suggestions there, but we want to gain more experience and be ready to answer that because we are currently working on this and don't have all the answers yet.

    The tool is well organized. What I am very happy about is the ease of deploying new resources. You can easily create your pipeline within minutes.

    What needs improvement?

    One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. 

    The tool should keep on updating new algorithms and not stay static. 

    For how long have I used the solution?

    I have been working with the product for ten years. 

    What do I think about the stability of the solution?

    I rate Microsoft Azure Machine Learning Studio's stability as nine out of ten. 

    What do I think about the scalability of the solution?

    I rate the solution's scalability a ten out of ten. I am the single user of Microsoft Azure Machine Learning Studio. 

    How are customer service and support?

    We haven't had any experience with the tool's support because we didn't use it. We are mature developers and don't need it at this time. We don't have any complex business needs.

    How was the initial setup?

    The tool's deployment time depends on the resource you will deploy. Some resources are deployed within minutes, while others may take more than 15-20 minutes. I have deployed mostly web applications, REST APIs, and databases.

    What was our ROI?

    We're trying to provide robust solutions to our customers, which previously involved multiple steps. Now, we're going to provide it in one step. That is our benefit because the customer will get a final solution, not a solution in steps. We will formalize and streamline them to align with our new solutions.

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

    We pay only the Azure costs for what we use, which involves some subscription costs. But essentially, you pay for what you use. There are no extra costs in addition to the standard licensing fees.

    What other advice do I have?

    We are trying to find some commercial value. I have learned how to use it, and we will integrate it into the project. That's our next goal.

    I rate Microsoft Azure Machine Learning Studio a ten out of ten. If you want to use it, get the certifications, and then work on some projects to gain more experience.

    Disclosure: My company does not have a business relationship with this vendor other than being a customer.
    PeerSpot user
    Buyer's Guide
    Download our free Microsoft Azure Machine Learning Studio Report and get advice and tips from experienced pros sharing their opinions.
    Updated: June 2025
    Buyer's Guide
    Download our free Microsoft Azure Machine Learning Studio Report and get advice and tips from experienced pros sharing their opinions.