Compare IBM SPSS Statistics vs. Microsoft Azure Machine Learning Studio
IBM SPSS Statistics is ranked 10th in Data Science Platforms with 4 reviews while Microsoft Azure Machine Learning Studio is ranked 5th in Data Science Platforms with 7 reviews. IBM SPSS Statistics is rated 7.8, while Microsoft Azure Machine Learning Studio is rated 7.2. The top reviewer of IBM SPSS Statistics writes "Anomaly detection, with a useful algorithm handbook, but it's not scalable". 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 Statistics is most compared with IBM SPSS Modeler, Weka and MathWorks Matlab, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Alteryx and Amazon SageMaker.
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Most Helpful Review | |
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Quotes From Members
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
Pros | |
The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important. Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files. Most of the product features are good but I particularly like the linear regression analysis. It has helped our analyst unit deliver work with more transparency and confidence, given that we can always view the dataset in totality, after each step of data transformation. The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis. | The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses. The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure. Visualisation, and the possibility of sharing functions are key features. It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component. When you import the dataset you can see the data distribution easily with graphics and statistical measures. Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most. 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. The graphical nature of the output makes it very easy to create PowerPoint reports as well. |
Cons | |
The statistics should be more self-explanatory with detailed automated reports. Technical support needs some improvement, as they do not respond as quickly as we would like. I think the visualization and charting should be changed and made easier and more effective. Needs more statistical modelling functions. | Integration with social media would be a valuable enhancement. If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice. Operability with R could be improved. I would like to see modules to handle Deep Learning frameworks. I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated. Enable creating ensemble models easier, adding more machine learning algorithms. It could use to add some more features in data transformation, time series and the text analytics section. Microsoft should also include more examples and tutorials for using this product. |
Pricing and Cost Advice | |
We think that IBM SPSS is expensive for this function. Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure. | 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. 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. |
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Ranking  | |
Views 3,443 Comparisons 2,839 Reviews 3 Average Words per Review 429 Avg. Rating 8.0 | Views 9,383 Comparisons 7,824 Reviews 6 Average Words per Review 468 Avg. Rating 7.3 |
Top Comparisons | |
 Compared 27% of the time.  Compared 11% of the time.  Compared 11% of the time. |  Compared 22% of the time.  Compared 11% of the time.  Compared 11% of the time. |
Also Known As | |
| SPSS Statistics | Azure Machine Learning |
Learn | |
| IBM | Microsoft |
Overview | |
| Your organization has more data than ever, but spreadsheets and basic statistical analysis tools limit its usefulness. IBM SPSS Statistics software can help you find new relationships in the data and predict what will likely happen next. Virtually eliminate time-consuming data prep; and quickly create, manipulate and distribute insights for decision making. | 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. |
Offer | |
Learn more about IBM SPSS Statistics | Learn more about Microsoft Azure Machine Learning Studio |
Sample Customers | |
| LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA | Information Not Available |
Top Industries | |
No Data Available | Software R&D Company30% Comms Service Provider18% Manufacturing Company6% Energy/Utilities Company5% |
Find out what your peers are saying about Alteryx, Knime, IBM and others in Data Science Platforms. Updated: November 2019.
383,162 professionals have used our research since 2012.
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