IBM SPSS Modeler vs Microsoft Azure Machine Learning Studio comparison

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Executive Summary

We performed a comparison between IBM SPSS Modeler and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.

Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed IBM SPSS Modeler vs. Microsoft Azure Machine Learning Studio Report (Updated: March 2024).
765,234 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The ease of use in the user interface is the best part of it. The ability to customize some of my streams with R and Python has been very useful to me, I've automated a few things with that.""A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly.""We have integration where you can write third-party apps. This sort of feature opens it up to being able to do anything you want.""Our business units' capabilities with SPSS Modeler is high. They no longer waste time on modeling and algorithms, meaning they are not coding any more. For example, segmentation projects now take one to three months, rather than six months to a year, as before.""We have a local representative who specializes in SPSS. He will help us do the PoC.""Our go live process has been slightly enhanced compared to the previous programmatic process. There is now a faster time to production from the business end.""The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well.""The visual modeling capability is one of its attractive features."

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"One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option.""MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse.""Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful.""Visualisation, and the possibility of sharing functions are key features.""The solution is really scalable.""The UI is very user-friendly and that AI is easy to use.""The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant.""Microsoft Azure Machine Learning Studio is easy to use and deploy."

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Cons
"It would be good if IBM added help resources to the interface.""It's not as user friendly as it could be.""When I used it in the office, back in the day, we did have some stability issues. Sometimes it just randomly crashed and we couldn't get good feedback. But when I use it for my own stuff now I don't have any problems.""I can say the solution is outdated.""I would not rate the technical support very well. The technicians have accents. When you do find someone, it is very hard to get somebody able to answer the technical questions.""Requires more development.""Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization.""I would like see more programming languages added, like MATLAB. That would be better."

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"The interface is a bit overloaded.""A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer.""The product must improve its documentation.""It would be nice if the product offered more accessibility in general.""I think it should be made cheaper for certain people…It may appear costlier for those who don't consider time important.""Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me.""Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier.""They should have a desktop version to work on the platform."

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Pricing and Cost Advice
  • "Having in mind all four tools from Garner’s top quadrant, the pricing of this tool is competitive and it reflects the quality that it offers."
  • "If you are in a university and the license is free then you can use the tool without any charges, which is good."
  • "It is a huge increase to time savings."
  • "The scalability was kind of limited by our ability to get other people licenses, and that was usually more of a financial constraint. It's expensive, but it's a good tool."
  • "When you are close to end of quarter, IBM and its partners can get you 60% to 70% discounts, so literally wait for the last day of the quarter for the best prices. You may feel like you are getting robbed if you can't receive a good discount."
  • "It got us a good amount of money with quick and efficient modeling."
  • "$5,000 annually."
  • "This tool, being an IBM product, is pretty expensive."
  • More IBM SPSS Modeler Pricing and Cost Advice →

  • "To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS."
  • "When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly."
  • "From a developer's perspective, I find the price of this solution high."
  • "The licensing cost is very cheap. It's less than $50 a month."
  • "There is a license required for this solution."
  • "I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
  • "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."
  • More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:Compared to other tools, the product works much easier to analyze data without coding.
    Top Answer:The platform's cloud version needs improvements. The process to access workflow could be user-friendly. It could be easier to log in and manage security levels. Additionally, it needs to be more… more »
    Top Answer:Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with… more »
    Ranking
    11th
    Views
    3,297
    Comparisons
    2,631
    Reviews
    6
    Average Words per Review
    372
    Rating
    7.3
    2nd
    Views
    15,820
    Comparisons
    12,975
    Reviews
    23
    Average Words per Review
    494
    Rating
    7.6
    Comparisons
    Also Known As
    SPSS Modeler
    Azure Machine Learning, MS Azure Machine Learning Studio
    Learn More
    IBM
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    Overview

    IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions from the desktop or within operational systems.

    Buy
    https://www.ibm.com/products/spss-modeler/pricing
     
    Sign up for the trial
    https://www.ibm.com/account/reg/us-en/signup?formid=urx-19947


    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

    Sample Customers
    Reisebªro Idealtours GmbH, MedeAnalytics, Afni, Israel Electric Corporation, Nedbank Ltd., DigitalGlobe, Vodafone Hungary, Aegon Hungary, Bureau Veritas, Brammer Group, Florida Department of Juvenile Justice, InSites Consulting, Fortis Turkey
    Walgreens Boots Alliance, Schneider Electric, BP
    Top Industries
    REVIEWERS
    University23%
    Financial Services Firm17%
    Manufacturing Company14%
    Government9%
    VISITORS READING REVIEWS
    Educational Organization16%
    Financial Services Firm10%
    Computer Software Company9%
    University8%
    REVIEWERS
    Financial Services Firm17%
    Energy/Utilities Company13%
    Retailer9%
    Comms Service Provider9%
    VISITORS READING REVIEWS
    Financial Services Firm12%
    Computer Software Company10%
    Manufacturing Company8%
    Healthcare Company7%
    Company Size
    REVIEWERS
    Small Business19%
    Midsize Enterprise10%
    Large Enterprise71%
    VISITORS READING REVIEWS
    Small Business23%
    Midsize Enterprise13%
    Large Enterprise64%
    REVIEWERS
    Small Business30%
    Midsize Enterprise11%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise72%
    Buyer's Guide
    IBM SPSS Modeler vs. Microsoft Azure Machine Learning Studio
    March 2024
    Find out what your peers are saying about IBM SPSS Modeler vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: March 2024.
    765,234 professionals have used our research since 2012.

    IBM SPSS Modeler is ranked 11th in Data Science Platforms with 38 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 47 reviews. IBM SPSS Modeler is rated 8.0, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". IBM SPSS Modeler is most compared with KNIME, Microsoft Power BI, RapidMiner, IBM SPSS Statistics and SAS Enterprise Miner, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and Google Cloud AI Platform. See our IBM SPSS Modeler vs. Microsoft Azure Machine Learning Studio report.

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    We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.