Machine learning lifecycle support for enterprise teams
Seamless building, training, scoring, and model management with StreamAnalytix
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
Visual pipelines to support model building, training, and scoring

Use pre-built operators or code in your preferred language – Python, Java, Scala, or H2O. Import your pre-built models from other platforms like TensorFlow and operationalize them within StreamAnalytix with ease.