Key Features

• Visual Data Preparation: Drag-and-drop interface for data cleaning and preparation
• Machine Learning: Built-in ML algorithms with AutoML capabilities
• Code Integration: Support for Python, R, SQL, Scala, and other languages
• Collaboration Tools: Team workspace for data scientists, analysts, and business users
• MLOps: Model deployment, monitoring, and lifecycle management
• Data Visualization: Interactive charts and dashboards
• API Integration: REST APIs for seamless integration

AI-Assisted Development Features

• Code suggestions and auto-completion
• Automated data profiling and quality assessment
• Smart recipe recommendations
• Automated feature engineering suggestions
• Model explanation and interpretability tools

Use Cases

• Predictive analytics
• Customer segmentation
• Fraud detection
• Recommendation systems
• Time series forecasting