Litify
The legal operating system built on Salesforce for high-growth firms and corporate departments.
Accelerate the path to predictive insights with a visual data science workbench for hybrid cloud.
IBM SPSS Modeler is a leading visual data science and machine learning (ML) solution designed to help organizations accelerate time to value by operationalizing AI. Its technical architecture centers on a canvas-based 'stream' interface, allowing data scientists and business analysts to construct complex analytical pipelines without extensive coding. By 2026, Modeler has been deeply integrated into the IBM watsonx.ai ecosystem, bridging the gap between traditional statistical modeling and modern generative AI workflows. It excels in handling large-scale data through SQL pushback technology, which offloads data processing directly to the database layer, minimizing data movement and latency. The platform supports a wide array of algorithms, from standard regression to sophisticated deep learning and geospatial analytics. Its market position is solidified as the go-to tool for enterprise-grade reliability, particularly in regulated industries like banking and healthcare, where model governance, explainability, and integration with legacy mainframe or modern cloud-native databases are critical. The tool effectively democratizes data science by providing automated modeling features (Auto Classifier/Auto Numeric) while maintaining extensibility for experts via Python and R scripting nodes.
Automatically translates Modeler stream logic into native SQL queries for execution within the database engine.
The legal operating system built on Salesforce for high-growth firms and corporate departments.
Empowering investment and credit management with AI-driven operational alpha and cloud-native agility.
The legal industry's gold standard for AI-driven research, analytics, and Shepard’s® validated insights.
A fast, distributed, high-performance gradient boosting framework based on decision tree algorithms.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Tests multiple classification algorithms (Logistic, Random Forest, SVM, etc.) in a single run and ranks them by accuracy.
Uses NLP and linguistic processing to extract concepts and sentiments from unstructured text data.
Allows users to write custom Python or R code directly within the stream to extend functionality with libraries like Scikit-learn or ggplot2.
Incorporates location data (latitude/longitude) to analyze spatial patterns and proximity-based predictions.
Exports models directly to Watson Machine Learning for deployment, monitoring, and bias detection.
Analyzes relationships between entities to identify influencers and community clusters.
High attrition rates among high-value retail banking customers.
Registry Updated:2/7/2026
Export list of high-risk customers to CRM via SQL export node.
Stockouts and overstocking across 500+ locations.
Unscheduled downtime on assembly lines costing $50k/hour.