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What's New in RapidMiner Server 7.5?

This page describes the new features of RapidMiner Server 7.5 as well as its enhancements.

The first iteration of new data core that manages data sets in a much more efficient way has arrived! This results in both better performance and less memory usage for the vast majority of operators.
Added support for Microsoft Azure Blob Storage with Read Azure Blob Storage, Write Azure Blob Storage, and Loop Azure Blob Storage operators. They work exactly like their existing Amazon S3 counterparts.
Added support for Amazon Key Management Service (AWS KMS) for all Amazon S3 operators. You can now optionally add an encryption key id to your Amazon S3 connection to decrypt/encrypt files when working with Amazon S3.

Remove Duplicates now features a new output port called duplicates which returns the examples identified as duplicates.
Fixed memory leaks for Handle Exception, Select Subprocess, and Branch.
Execute Script now caches the parsed scripts for significantly faster execution, especially inside Loop operators or other highly concurrent environments. General performance of script execution has also been improved. Also added operator tags and added a default example script to make usage of the operator easier.
Improved AutoMLP performance.
Added new global process macro: %{process_start} which captures the timestamp when a process was started.
Server repository zip dump export and import functionality streamlined
Server compatibility with RapidMiner Studio 7.5

Fixed a bug making all parallel Loop operators incredibly resource hungry when running hundreds of thousands of iterations
Removed empty confidence columns when applying the model from Linear Discriminant Analysis, Quadratic Discriminant Analysis, Regularized Discriminant Analysis, Single Rule Induction, Subgroup Discovery
Regular Discriminant Analysis no longer ignores the alpha parameter
The median for Aggregate now takes the middle point of both middle values in case of an even number of values
Fixed broken configurable entries due to unnecessary escaping
Fixed major memory leak for Loop, Loop Values, Loop Attributes, and Loop Files
Fixed behaviour of Fast Large Margin if learned with bias (parameter)
Fixed Linear Regression calculation in case use bias is not selected
Fixed confidences of Ada Boost in border cases
Logistic Regression and Generalized Linear Model no longer allow p-value calculation without adding intercept

Changes to Fast Large Margin might affect behaviour of models learned with prior versions of RapidMiner. If you have an existing Fast Large Margin model which was learned using bias, we suggest you learn the model again with this release to ensure correct predictions.