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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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
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 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.
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.
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.
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.
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Feature List
System Requirements
Processor | 3 GHz or faster, Quad core (2 GHz, Dual core, minimum) |
RAM | 32GB to 1TB RAM (8GB RAM, minimum) |
Hard Disk | Enough 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 System | Windows Server 2019 Windows Server 2016 Windows Server 2012 R2 Windows Server 2012 Windows Server 2008 R2 Linux |
Java platform | 64-bit recommended Oracle Java 8 or OpenJDK 8 (official distribution or e.g. AdoptOpenJDK) |
Operational Databases | PostgreSQL 9.6, 10.9, 11.4 Microsoft SQL Server 2017 Oracle 12c (compressed tables are not supported) MySQL 5.6, 5.7, 8.0 |