RapidMiner AI Hub

RapidMiner for teams – accelerate machine learning model building, automate RapidMiner Studio processes, R or Python code, collaborate, scale and deploy.

RapidMiner AI Hub

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Machine learning team collaboration

Team Collaboration

Share, manage, and secure data prep and machine learning modeling processes, all work and data science artifacts (connections, data, processes, models and other results) in a central repository.

Configure granular permissions to control access to specific processes and folders.

RapidMiner Go (included with RapidMiner Auto Hub) scales data science across the enterprise with browser-based automated ML that’s built for business users

RapidMiner Server process automation

Process Automation

Automate important tasks as often as needed.

Create scheduled processes to prep and clean data, retrain models, and continuously score data in real-time

Integrate with external applications through a REST API

Accelerate Machine Learning Model Creation

Accelerate Machine Learning Model Building

Use dedicated server hardware to radically speed up predictive model creation.

Take full advantage of multi-core multiprocessor server architectures, on prem. or in the cloud with Azure or AWS.

Push jobs from RapidMiner Studio to RapidMiner AI Hub in a single click. Scale up and accelerate Auto Model, as you run hundreds of models in parallel.

Operationalise predictive models

Turn Insight into Action

Operationalise predictive models and turn prescriptive actions into prescriptive recommendations.

Create production web service APIs in just a few mouse clicks.

Monitor model performance over time to detect degradation and retrain as needed.

RapidMiner Real-Time Scoring

Deploy models to RapidMiner Real-Time Scoring for high volume, low latency scoring

Support demanding real-time scoring use cases.

Deliver predictions with near-zero latency in just a few milliseconds.

Deploy on-premise or in the cloud via Amazon Web Services or Microsoft Azure.

RapidMiner with JupiterHub

Code in the RapidMiner platform with JupyterHub

We’ve made it easier for full-time coders to work more collaboratively with non-coders by co-deploying JupyterHub with RapidMiner AI Hub. This includes SSO and easy connection to the repository.


Grafana to easily create visualizations from your RapidMiner results

Grafana is a very popular open-source solution for data visualization that’s easy to use and offers a wide variety of visualizations. We’ve packaged in a Docker container, so anyone can easily build interactive dashboards to share insights and analysis with their colleagues.

Request pricing

Let’s create a plan for your specific needs.

Feature List

Computation and Scalability

  • Computational service: Use the computational power of enterprise servers and free Studio resources for development
  • Virtually unlimited scalability: Create AI Hub clusters of any size by adding more Job Agents and machines to the environment


  • Easy creation of queues for resource management: Resources can be split among users or use cases, they can be shared or made exclusive by the administrator depending on the company’s structure
  • Flexible environment configuration: multiple options when creating queues that can adapt to any environment
  • A new “clean-up” button can reset the Server queue and stop all running and pending jobs, leaving the AI Hub in a clean state, ready to start anew.


  • Periodic scheduling of workflow executions within the AI Hub’s UI
  • Set up actions based on a triggered event
  • Remote execution of analysis processes

Integration & Operationalisation

  • One-click deployment using Web Services: any process can be readily made into a published web service. Web Services allow integration with third-party tools like custom web consoles, BI tools and others.
  • Web services / processes can deliver XML, JSON, static / dynamic visualizations and binary files among others
  • The Real-time scoring agent allows you to predict at scale, with very low latency, and deliver actionable intelligence in real-time


  • Shared repository with security controls. Groups can share or protect their models and processes as needed
  • “Copy&paste” processes from Studio to AI Hub or from Server to Server
  • Fine-grained permissions for processes, models and data


  • Reusable templates and processes: make processes available to the whole team to act as templates or best practices
  • LDAP integration or RapidMiner user system
  • Process version management

Management and Monitoring

  • Webapps: custom dashboards for monitoring, management and showcasing of process results
  • Logging and auditing of executions
  • Monitoring of current schedules and executions


  • Define and protect your database connections for processes to securely access data

Platform Deployment and Operation

  • Deploy anywhere and operate with minimal effort: on-premise, in the cloud or even in hybrid setups
  • Install easily, run pre-built images from Docker Hub or spin up ready-to-use VMs from the Microsoft Azure or AWS marketplaces
  • License flexibly and deploy to your needs
System Requirements
Processor3 GHz or faster, Quad core  
(2 GHz, Dual core, minimum)
(8GB RAM, minimum)
Hard DiskEnough free disk space (the filesystem needs to support UTF-8) for users to store data in the server’s repository
(>10GB free disk space, minimum, the filesystem needs to support UTF-8)
Operating SystemWindows Server 2019
Windows Server 2016
Windows Server 2012 R2
Windows Server 2012
Windows Server 2008 R2
Java platform64-bit recommended
Oracle Java 8 or
OpenJDK 8 (official distribution or e.g. AdoptOpenJDK)
Operational DatabasesPostgreSQL 9.6, 10.9, 11.4
Microsoft SQL Server 2017
Oracle 12c (compressed tables are not supported)
MySQL 5.6, 5.7, 8.0