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).
763,955 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
"It continues to be a very flexible platform, so that it handles R and Python and other types of technology. It seems to be growing with additional open-source movement out there on different platforms.""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.""It helped me in that I didn't need to write them by hand, and I could get a result in one or two minutes. That helped me a lot.""IBM was chosen because of usability. It's point and click, whereas the other out-of-the box-solution, or open-source solutions, require full-on programming and a much higher skill level.""The visual modeling capability is one of its attractive features.""​It works fine. I have not had any stability issues; it is always up.​""It scales. I have not run into any challenges where it will not perform.​""It is a great product for running statistical analysis."

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"Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful.""It helps in building customized models, which are easy for clients to use​.​​""ML Studio is very easy to maintain.""Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon.""The product supports open-source tools.""It's a great option if you are fairly new and don't want to write too much code.""The most valuable feature of the solution is the availability of ChatGPT in the solution.""The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."

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Cons
"The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood.""The platform that you can deploy it on needs improvement because I think it is Windows only. I do not think it can run off a Red Hat, like the server products. I am pretty sure it is Windows and AIX only.""I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it.""​I would like better integration into the Weather Company solution. I have raised a couple of concerns about this integration and having more time series capabilities.​""​Initial setup of the software was complex, because of our own problems within the government.""The challenge for the very technical data scientists: It is constraining for them.​""We would like to see better visualizations and easier integration with Cognos Analytics for reporting.""The forecasting could be a bit easier."

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"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me.""Enable creating ensemble models easier, adding more machine learning algorithms.""When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers.""The data preparation capabilities need to be improved.""It would be great if the solution integrated Microsoft Copilot, its AI helper.""While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy.""The platform's integration feature could be better.""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."

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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 Company11%
    Manufacturing Company8%
    Healthcare Company7%
    Company Size
    REVIEWERS
    Small Business19%
    Midsize Enterprise10%
    Large Enterprise71%
    VISITORS READING REVIEWS
    Small Business23%
    Midsize Enterprise14%
    Large Enterprise63%
    REVIEWERS
    Small Business31%
    Midsize Enterprise9%
    Large Enterprise60%
    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.
    763,955 professionals have used our research since 2012.

    IBM SPSS Modeler is ranked 11th in Data Science Platforms with 6 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 22 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 "Useful visual programming, minimal configuration required, and overall powerful". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Has a drag and drop feature and easier learning curve, but the number of algorithms available could still be improved". 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.