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."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."
"Very good data aggregation."
"It will scale up to anything we need."
"New algorithms are added into every version of Modeler, e.g., SMOTE, random forest, etc. The Derive node is used for the syntax code to derive the data."
"We had an IBM Guardium service contract where we used one of their resources to help us develop our prototype. It was a good experience, but they were helpful and responsive."
"The quality is very good."
"So far, the stability has been rock solid."
"It scales. I have not run into any challenges where it will not perform."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"The most valuable feature is its compatibility with Tensorflow."
"The solution is very easy to use, so far as our data scientists are concerned."
"When you import the dataset you can see the data distribution easily with graphics and statistical measures."
"The solution is really scalable."
"I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model."
"I like being able to compare results across different training runs. The hyperparameter tuning function is a valuable feature because it provides the ability to run multiple experiments at the same time and compare results."
"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses."
"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."
"The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood."
"The product does not have a search function for tags."
"If IBM could add some of the popular models into the SPSS for further analysis, like popular regression models, I think that would be a helpful improvement."
"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."
"It is not integrated with Qlik, Tableau, and Power BI."
"Time Series or forecasting needs to be easier. It is a very important feature, and it should be made easier and more automated to use. For instance, for logistic regression, binary or multinomial is used automatically based on the type of the target variable. I wish they can make Time Series easier to use in a similar way."
"Dimension reduction should be classified separately."
"The initial setup time of the containers to run the experiment is a bit long."
"The price could be improved."
"The platform's integration feature could be better."
"It would be nice if the product offered more accessibility in general."
"I would like to see modules to handle Deep Learning frameworks."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."
"The price of the solution has room for improvement."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 52 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 Microsoft Power BI, KNIME, IBM SPSS Statistics, RapidMiner and Weka, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Google Cloud AI Platform. See our IBM SPSS Modeler vs. Microsoft Azure Machine Learning Studio report.
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