IBM SPSS Modeler is a leading visual data science and machine learning (ML) solution designed to help enterprises accelerate time to value by speeding up operational tasks for data scientists. Organizations worldwide use it for data preparation and discovery, predictive analytics, model management and deployment, and ML to monetize data assets.
IBM SPSS Modeler is also available within IBM Cloud Pak for Data, a containerized data and AI platform that enables you to build and run predictive models anywhere — on any cloud and on premises. IBM Cloud Pak for Data as a Service enables you to run Modeler flows on the public cloud. You can try it today at no cost with no download required.
- Take advantage of open source-based innovation, including R or Python.
- Empower data scientists of all skills — programmatic and visual.
- Explore a hybrid approach — on premises and in the public or private cloud.
- Start small and scale to an enterprise-wide, governed approach.
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Start with GUI-based data science and machine learning algorithms.
Empower coders, non-coders and analysts.
Start small and expand with enterprise-class security and governance.
IBM SPSS Modeler automatically transforms data into the best format for the most accurate predictive modeling. It now only takes a few clicks for you to analyze data, identify fixes, screen out fields and derive new attributes.
From Scikit-learn and TensorFlow to IBM SPSS Modeler, save and deploy models from the most popular machine learning frameworks.
Leverage IBM SPSS Modeler’s powerful graphics engine to bring your insights to life. The smart chart recommender finds the perfect chart for your data from among dozens of options, so you can share your insights quickly and easily using compelling visualizations.
IBM SPSS Modeler automatically transforms data into the best format for the most accurate predictive modeling. It now only takes a few clicks for you to analyze data, identify fixes, screen out fields and derive new attributes.
IBM SPSS Modeler supports decision trees, neural networks and regression models. Now you can take advantage of ARMA, ARIMA and exponential smoothing; transfer functions with predictors and outlier detection; benefit from ensemble and hierarchical models; support vector machine and temporal causal modeling; and employ time series and spatial AR for spatiotemporal prediction. Generative adversarial networks (GANs) and reinforcement also enable deep learning.
IBM SPSS Modeler enables the use of R, Python, Spark and Hadoop to amplify the power of analytics. You can also extend and complement these technologies for more advanced analytics while maintaining control.
Read how IBM SPSS Modeler was used to help reduce manufacturing defect rates at Kyocera Corporation
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Read how IBM SPSS Modeler helped Kyocera Corporation achieve a 6% increase in yield by reducing defects.
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