Automated Data Science

Automate the end-to-end process of feature engineering and selection by using in-built capabilities. Make use of AutoML to achieve the best possible model performance and improve it further with automated hyperparameter tuning. Leverage the one-click option to deploy the model once it’s developed and evaluated

Features

• Automated Feature Engineering - Create various features automatically from different datasets by analyzing the relationship between columns and tables - Generate new features with the automated handling of the depth of the relationship between datasets - Add custom rules while creating new features
• Automated Feature Selection - Achieve better model performance using ML-based feature selection from thousands of features to choose from - Get automated feature validation options based on the type of modeling algorithm
• High-performance Auto ML - Get the best-suited model performance, with ML auto-selecting ideal pre-processing modeling algorithms and parameter configuration - Improve model performance with automated hyper parameter tuning through several optimization techniques, such as grid search, Bayesian optimization, etc. - Optimize the computation costs by using time-bound auto ML capabilities
• Automated Model Deployment and Retraining - Ensure one-click deployment once the model is developed and evaluated - Leverage auto model retraining and selection based on a predefined frequency or an event