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RapidMiner
RapidMiner is an open source software platform for data science teams that unites data prep, machine learning, and predictive model deployment. It operates through visual programming and is capable of manipulating, analysing and modelling data. Its unified data science platform accelerates the building of complete analytical workflows – from data prep to machine learning to model validation to deployment – in a single environment, dramatically improving efficiency and shortening the time to value for data science projects
Type of content:
Assets
Type of asset:
Platform
Big data potential
Yes
Phase in the policy cycle:
Policy Design and Analysis
TRL
8
Implementation/customisation cost
High
Open license availability
No
Ease of use
High
Website
Tags:
Open Data
IT
IT processes
IT architecture
Serves:
| SWOT Analysis for RapidMiner |
||
|---|---|---|
| Helpful | Harmful | |
Internal |
Strengths• Visual workflow for predictive analytics: Visual Workflow Design. Quick-to-learn and easy-to-use drag & drop approach accelerates end-to-end data science for improved productivity • Build predictive models, faster • Intuitive data prep • Collaboration, management, and deployment • Real-Time Scoring: Turn insight into action • Code free data science for Hadoop and Spark • Unified Platform. One platform, one user interface, one system, support the complete workflow from data prep, through model deployment to ongoing model management • Breadth of Functionality. More pre-defined machine learning functions and third-party libraries than any other visual platform • Open Source Innovation. Well-accepted open languages and technology, a community of over 250K data science experts, and a robust marketplace keeps pace with evolving data science requirements • Broad Connectivity. More than 60 connectors provide easy access to all types of data: structured, unstructured & big data • Data Science at Every Scale. Run workflows in-memory or in-hadoop, providing the best option for projects of all sizes. • 400000 users |
Weaknesses• High implementation /customisation cost • Competition • Storage and processing requirements. • More tutorials/samples needed • Limited partitioning abilities for dataset to training and testing sets • Doesn’t allow changes on the behaviour of a machine learning algorithm that already exists in the repository |
External |
Opportunities• Churn Prevention: Identify customers likely to leave, take preventative action. • Customer Lifetime Value: Distinguish between customers based on business value. • Customer Segmentation: Create meaningful customer groups for more relevant interactions. • Demand Forecasting: Know what volumes to expect to improve planning. • Fraud Detection: Identify fraudulent activity quickly and end it. • Next Best Action: The right action at the right time for the right customer. • Predictive Maintenance: Predict equipment failure, plan cost-effective maintenance. • Price Optimisation: Set prices that balance demand, profit, and risk. • Product Propensity: Predict what your customers will buy, before even they know it. • Quality Assurance: Resolve quality issues before they become a problem. • Risk Management: Understand risk to manage it. • Up- and Cross-Selling: Convince customers to buy more. • Automatic programming • Innovation |
Threats• High implementation /customisation cost • Competition • Storage and processing requirements. • Limited partitioning abilities for dataset to training and testing sets • Doesn’t allow changes on the behaviour of a machine learning algorithm that already exists in the repository |
