Home > Categories > Machine learning and neural networks > ML Platforms > RapidMiner

RapidMiner

Rating:

4.5 / 5.0

Tags

data science, machine learning, AI, predictive analytics, data mining, AutoML, visual workflow, enterprise AI, MLOps

Categories:
Machine learning and neural networks
Creator RapidMiner
Date 2006-01-01
Platforms Desktop, Server, Cloud
Status Active
Website rapidminer.com
Price Model Freemium / Subscription
Sections:
Automated ML (AutoML)ML PlatformsModel DeploymentModel Training

Pricing Details

Available via subscription (Professional, Enterprise, AI Hub). Free version (RapidMiner Studio Free) with limitations. Prices start around ~$7,500/year.

Features

Visual workflow designer; Drag-and-drop interface; AutoML capabilities; Data preparation tools; Predictive modeling; Text mining; Integration with various data sources (databases, cloud storage); Wide range of ML algorithms; Model validation and optimization; Model deployment and management (AI Hub); Collaboration features (AI Hub); Explainable AI (XAI).

Integrations

Integration with databases (Oracle, MySQL, SQL Server, PostgreSQL, etc.); Integration with cloud storage (Amazon S3, Azure Blob Storage, Google Cloud Storage, Dropbox); Integration with applications (Salesforce, Twitter, Zapier); Integration with BI tools (Tableau); API for custom integration; Python integration.

Preview

RapidMiner is a comprehensive data science platform that covers the entire machine learning project lifecycle, from data preparation and visualization to model building, validation, and deployment. The platform is known for its intuitive visual workflow designer with a drag-and-drop functionality, which allows creating complex analytical pipelines without writing code, making it accessible to a wide range of users, including business analysts. RapidMiner actively uses AI and AutoML to automate routine tasks such as algorithm selection, hyperparameter tuning, and feature engineering, accelerating the model development process. The platform supports integration with various data sources, including databases, cloud storage, and third-party applications. It offers a wide range of operators for data preprocessing, statistical analysis, predictive modeling, and text analytics. RapidMiner is applied in various industries such as finance (fraud detection, credit risk assessment), retail (customer segmentation, marketing campaign optimization), healthcare (medical data analysis), and manufacturing (predictive maintenance). In addition to RapidMiner Studio for desktop development, the platform includes RapidMiner AI Hub for collaboration, automation, and model deployment, and RapidMiner Go for automated machine learning.