RapidMiner 9.10 takes important steps towards responsible AI while helping analytics teams accelerate time-to-value for streaming & IIOT use cases.
Visual Workflow Designer
Increase productivity across the entire data science team, from analysts to experts
Speed up and automate the creation of predictive models in a drag + drop visual interface
Rich library of 1500+ algorithms and functions ensures the best model for any use case
Pre-built templates for common use cases including customer churn, predictive maintenance, fraud detection, and many more
“Wisdom of Crowds” provides proactive recommendations at every step to help beginners

Connect to Any Data Source
Work with all of your data, no matter where it lives
Instantly create point + click connections to databases, enterprise data warehouses, data lakes, cloud storages, business applications and social media
Easily re-use connections any time and easily share them with anyone who needs access
Connect to new sources with extensions from the RapidMiner Marketplace

Automated In-database Processing
Run data prep and ETL inside databases to keep your data optimized for advanced analytics
Query and retrieve data without writing complex SQL
Harness the power of highly scalable database clusters
Supports MySQL, PostgreSQL, and Google BigQuery
What's New and Bugs Fixes
New Features
- Bias detection & mitigation: Receive bias warnings in every part of the RapidMiner platform including Turbo Prep, Model Simulator and more. When Studio thinks you have a column that could lead to model bias, you’ll receive a warning along with an in-platform callout that explains what it was triggered by.
- Streaming & IIOT advancements: Mix and match RapidMiner with Python in low latency (50-100ms) use-cases, such as scoring large volumes of sensor data. Additionally, leverage a new function-fitting operator to fit data with custom functions when creating models for anomaly detection on devices, modeling physical behavior based on data, and more.
- Security enhancements: Support for Docker Rootless mode along with enhanced security in Kubernetes environments both raise our overall security standards. Security for containerized platforms is also improved through regular updates of Docker images with the newest secure components.
- Time series forecasting: Automate forecasting future values of univariate time series based on historical data in RapidMiner Go. Track advanced and seasonal trends when forecasting sales or staffing requirements and use intuitive visualizations to compare the results of competing models.
- NLP extension: Leverage a new RapidMiner extension for natural language processing to extract part-of-speech tags and recognize people, cities, organizations, and other entities within free text. This is typically used as a pre-processing method to determine the contents of documents, website text, etc.
Bugfixes
- The core Pivot operator now runs as expected inside a SparkRM operator.
- Updated heuristics for Hive table reads in Radoop Spark jobs to prevent failing Spark jobs when hidden Hive staging directories are present