You are viewing the RapidMiner Studio documentation for version 9.3 - Check here for latest version
What's new in RapidMiner Studio 9.3
Improved team collaboration with the new way of managing data connectivity
Easily and securely create and share the new repository-based connections. As a user, you can keep your credentials in a Vault that’s not accessible to anyone else. Deploy use cases from development to production by moving connections from Server to Server, while they get re-configured on the fly. You’ll be able to scale with your needs, as connections can be created once and shared with multiple users.
Better collaboration with coder data scientists
Augment your RapidMiner toolset with anything you can do in Python – use Python whenever there is a need. Use the best for the job – do parts of the data science process in RapidMiner and others in Python. Access RapidMiner artifacts and re-use work done in RapidMiner from Python.
Accelerate and scale Auto Model in RapidMiner Studio by running model calculations on RapidMiner Server
Allows to
- Scale automated machine learning to large data sets leveraging the compute power of RapidMiner Server.
- Accelerate model training by running multiple algorithms in parallel on the distributed architecture of the RapidMiner Server platform.
- Free up Studio to work on multiple tasks in parallel while you run model calculation on Server.
- Easily select to run model computation tasks of Auto Model locally in Studio or on Server.
Improved time series analysis capabilities
Understand data better and discover hidden trends, patterns, periodicity, seasonality with the newly added Auto Correlation operator. Start your time series forecasting with default model to set a benchmark performance for future analysis. Improve forecast performance by fitting polynomial function to trend and seasonal components of time series data.
Enhancements and bug fixes
The following pages describe the new features, enhancements and bug fixes in RapidMiner Studio 9.3.1 releases: