Welcome to RapidMiner 9.10 BETA!

Dear RapidMiner User,

The focus for this release is on platform security, as well as covering some use cases, especially interesting for manufacturing, like function fitting to model physical behavior or direct python deployment.

Please try it and share your feedback with us.

Submit Feedback!

What's New

Fit data with custom functions

Fit your data using any custom parameterized function using least squares. This is especially useful in industrial cases where some information is already known expected relations between attributes:

Some possible use cases:

  • Model physical behaviour based on data
  • Confirm physical hypothesis on measured data
  • Anomaly detection on device behavior
  • ...and many others

Tighter security of your Docker deployment

The new Gen2 images, together with the new docker templates now support rootless deployments. Removing the need for a user with elevated permissions reduces the risk of a malicious control of the system.

Support for Openshift

Red Hat OpenShift is a container platform for Kubernetes that uses very high security standards. If Openshift is your primary deployment platform, now you can run RapidMiner on it. If it's not, read the next bullet point to see how this benefits you anyway.

Extended Kubernetes security

We have adopted best practices and security standards from Red Hat Openshift and implemented them in our Gen2 images and Kubernetes templates (Helms charts). This provides you with best-in-class security for your deployments.

Run and deploy Python code with low latency

RapidMiner StandPy is a set of always-on Python executor containers co-deployed with AI Hub, ready to run code defined in RapidMiner's Python operators. The latency is greatly reduced (from seconds to the 50-100 ms range), enough to support real-time use cases.

Improved collaboration features between RapidMiner Notebooks and our Python operators

Several small improvements add up to a significantly improved collaboration experience between users of RapidMiner Notebooks and RapidMiner Studio. Connections can now be used by the Python operators by simply connecting them to one if its input ports and then using the connection metadata in code. Reusing an entire notebook created with RapidMiner Notebooks is also much simpler, just by dragging the notebook file to the canvas in RapidMiner Studio. We have also changed how we handle paths in code executed in Python operators, enabling more complex use-cases such as organizing code into multiple files and referencing them in code in our Python operators.

New operators for the In-Database Processing Extension

Breakpoints inside the nest allow for better and easier development. "Replace Missing Values" and "Declare Missing Value" operators have been added. The extension is available for download from the RapidMiner Marketplace.

Radoop improvements

New Radoop connections: a very important feature addition. Shareable, secure Radoop connections using the new connection management framework. Admins can create and configure Radoop connections with the existing ease of use and convenience features that Radoop already offers, and can then export those connections to repositories or projects, where they can easily be shared with much less administrative overhead and no room for error. This also makes Radoop connection management a breeze in AI Hub deployments.

We have also published a new operator named Radoop Connection Test which makes troubleshooting connections easy, especially in AI Hub environments. The operator can also collect useful troubleshooting logs and store them in your project or repository, ensuring that our support organization can help you much faster if there's a problem you cannot solve.

Fully isolated Python environment control in Spark Scripting. Pack all your PySpark dependencies - including the Python interpreter version of choice - using Conda environment management - and use them on Hadoop cluster side without interfering with other users. Radoop supports Nest and Subprocess level isolation of Python environments in PySpark job submissions by opening up the possibility to use only those dependencies which perfectly fit the task, all the while saving the administration hassle and the need to compromise on a single and often incompatible set of preinstalled dependencies used by everyone within your organization.

Time Series: providing better performance and reliability.

Code base in time series is now using Belt.

New feature for Equalize Indices Operators (Numerical Indices, Time Stamps) to select a constant replacement value. This allows to fill gaps in non-equalized data sets with constant values.

OPC-UA Connector

Direct access to the common industry automation platform OPC-UA with RapidMiner.

New operators to read and monitor industry asset data and browse to discover assets in a system.

Changelog for RapidMiner Studio BETA

See Studio changelog.

Changes for RapidMiner Studio BETA

  • Added new Function Fitting operator that can optimize parameters in a function of the attributes to fit the label
  • The De-Normalization operator has a new parameter to also de-normalize predictions.
    • Based on attribute name: prediction(abc) tries to use de-normalization of abc if no explicit de-normalization available
    • The label (or other special attributes) can be included in normalization already in the normalize operator. The changes allow for multiple prediction attributes to be affected
  • Improved performance of Append operator
  • Handled yet another case of JDBC drivers ignoring the JDBC standard gracefully (here: Infor Data Lake DatabaseMetaData#getTypeInfo())
  • Introduced operator signatures to improve start up of Rapidminer
    • Signatures contain meta information that is used in operator registration, global search setup and documentation browser display
    • Signatures are persisted between starts for an improved start up time
    • Signature persistence can be configured or cleared with the setting System -> Local File Cache -> Keep Operator Signatures
  • Time Series: Enabled the usage of constant values for the replace types in the Equalize Numerical Indices and Equalize Time Stamps operators
    • The operators can now be used to fill gaps in non-equal data sets with constant values
  • Time Series: All Time Series Operators (except for Multi Horizon Forecast, Multi Horizon Performance) now working with Belt IOTable (as in- and output)
  • Fixed an issue where in rare instances, operator parameters did not get saved correctly if a default value was set for it. This e.g. affected date parameters used in extensions.
  • Generate Attributes max and min functions do now always return missing value if any of the values is missing.

Changelog for RapidMiner AI Hub/Server BETA

See AI Hub changelog.

Changes for RapidMiner AI Hub/Server BETA

  • Added admin only capability to manage vault entries for users via REST endpoint
  • Improved handling and logging for failed connection meta data generation of the legacy repository
  • Allow to use basic auth or OAuth2 as authentication method in RTS
    • Only one can be enabled concurrently
    • More information can be found in a fresh copy of your `agent.properties`
  • Default to `1` Job Container for the embedded Job Agent configuration
  • Fixed listing of new connections in repository

Documentation

We have set up a beta/RC documentation page where you will find additional information about the new features of this release. It is available at docs-beta.rapidminer.com.

Downloads

Below you can download the Beta/RC version of RapidMiner 9.10. Please note that your existing licenses will determine the products and functionality you are able to test.

RapidMiner Studio

Windows

Installation: Extract all contents of the ZIP archive and run RapidMiner-Studio.exe

Note that this release cannot be used to update existing installations!

Mac OS X

Installation: Open the disk image and drag the RapidMiner Studio 9.10 Preview App to your Applications folder.

Other Platforms

Installation: Extract all contents of the ZIP archive and either run RapidMiner-Studio.sh (Linux) or RapidMiner-Studio.bat (Windows).

Note that this release cannot be used to update existing installations!

RapidMiner AI Hub/Server

All Platforms

Installation: See our Installation Walkthrough for details. Note that we strongly advise against upgrading existing installations! Please install this preview release separately from any of your production or backup systems. Please contact us with any questions you may have regarding installation.

RapidMiner Radoop

All Platforms

Installation: Save the JAR file in your .RapidMiner/extensions directory (located in your user directory).

If you need to install RapidMiner Radoop functions (Hive UDFs) manually then please contact us to discuss the beta UDF upgrade on your Hadoop cluster.

RapidMiner Python Scripting Extension

All Platforms

Installation: Save the JAR file to your .RapidMiner/extensions directory (located in your user directory).

RapidMiner Platform Deployment

All Platforms

Installation: Download the installation package, and do the following steps:

  1. Unzip it on a machine which runs Docker.
  2. Edit the .env file in your favorite editor. Add the public URL of your machine to the PUBLIC_URL and SSO_PUBLIC_URL variables. It can be a hostname or an IP address, just make sure to prefix it with http:// or https://
  3. Optionally, provide your Server license key in the SERVER_LICENSE variable.
  4. Optionally, provide your Go license key in the GO_LICENSE variable.
  5. Issue docker-compose up -d rm-init-svc and wait a few minutes to complete.
  6. Issue docker-compose up -d.
  7. Point your browser to the IP address or hostname of your machine that you provided in step 2.

Please deploy this beta release separately from any of your production or backup systems. Please contact us with any questions you may have regarding installation.

Feedback

Your feedback is a critical to the success of our beta program and we are looking forward to your comments.

Please send all your feedback – positive or negative – via the “Submit Feedback” button below. Please submit separate reports for each new topic so that we are better able to track and address your comments.

For bugs or errors, please be as specific as possible when submitting your report:

  • How can we reproduce the error?
  • What UI elements were you interacting with?
  • Attach stack traces files that show the error.

    • RapidMiner Studio stores log files in .RapidMiner/rapidminer-studio.log and .RapidMiner/launcher.log. You can also enable the log view in RapidMiner Studio via View > Show Panel.
    • RapidMiner AI Hub/Server logs to .../rapidminer-server-home-directory/log/server.log (relative to its installation directory).
    • RapidMiner Radoop can export logs after a connection test using the Extract Logs... button on the Manage Radoop Connections dialog