Multiple machine learning models

Support for scoring a wide range of machine learning technologies like Spark, Python, H2O, and R

Data exploration

Interact with your data to uncover hidden structures, cleanse, and apply various transformation operators to explore co-relation between different variables

Apply data quality checks to ensure accurate model training

Model training

Rapidly train multiple big data machine learning models

Choose the optimum model for deployment Intuitive wizard-based model training process

Model calibration

Efficiently tune hyperparameters by training many models in parallel over a cluster

Automated selection of the best combination of parameters from all possible combinations of hyperparameters

Effective post-production monitoring

Apply A/B testing for monitoring model performance in the production environment

Swap the best performing model based on real-time performance or the accuracy using ‘Champion Challenger, Hot Swap’ paradigms

Easy operationalization of models

Easily manage the entire lifecycle of data science models – trained within the platform or using other technologies like Python, R, KNIME, or RapidMiner

Use features like model-as-a-service, model scaling, auditing, and model reproducibility