Multiple Big Data Machine Learning Models
Support for high performance scoring for a wide range of machine learning technologies like Spark, Python, H2O, and R
Data Science and Machine Learning
A visual data science platform that enables you to build, train, calibrate, deploy, and enable scoring of machine learning models easily, on both real-time and batch data.
Data Exploration
Interact with your data to uncover hidden structures, cleanse, and apply various transformation operators
On-the-fly charting capability to explore co-relation between different variables
Apply data quality checks to ensure accurate model training
Data Exploration
Interact with your data to uncover hidden structures, cleanse, and apply various transformation operators
On-the-fly charting capability 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 and choose the optimum model for deployment
An 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
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
Rapidly Deploy Models with Ease
Easily integrate the trained model into a production environment
Deploy an ensemble of models or combine various deployed models into an optimal model
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
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
Resources
data sheets
Leveraging continuous integration, delivery, and deployment(CI/CD) in StreamAnalytix
Continuous Integration and Delivery (CI/CD) is a set of automated SDLC practices and methods that enable frequent and error-free releases…
blog Sep 02, 2019
Detect and prevent insider threats with real-time data processing and machine learning
Insider threats are one of the most significant cybersecurity risks to banks today. These threats are becoming more frequent, more difficult to detect, and more complicated to prevent.
