Data Science and Machine Learning

A visual data science platform that enables you to easily build, train, calibrate, deploy and enable post-production monitoring of machine learning models, on both real-time and batch data

Ease of Building Big Data Machine Learning Models

Use pre-built advanced analytics and machine learning operators at scale, like: Spark MLlib, ML, model-porting standards like PMML, H2O and TensorFlow.

Easily prototype your own custom machine learning algorithm in the language of your choice (including Python and R).

Model Training and Calibration

Rapidly train multiple big data machine learning models, and choose the optimum model for deployment.

Efficiently tune hyper parameters by training many models in parallel over a cluster.

Model Training and Calibration

Rapidly train multiple big data machine learning models, and choose the optimum model for deployment.

Efficiently tune hyper parameters by training many models in parallel over a cluster.

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.

Learning and Execution of Data Science Algorithms

  • Real-time Scoring and Online Learning

Supports online model training, testing and model scoring, allowing data scientists to create real-time machine learning models and algorithms.

Connect the scoring output to the training pipeline to create real-time self-learning machine learning models.

  • Real-time Scoring and Offline Learning

Build the model using periodic batch jobs (e.g., Every 4 hours) and refresh the new model onto the scoring pipeline.

Learning and Execution of Data Science Algorithms

  • Real-time Scoring and Online Learning

Supports online model training, testing and model scoring, allowing data scientists to create real-time machine learning models and algorithms.

Connect the scoring output to the training pipeline to create real-time self-learning machine learning models.

  • Real-time Scoring and Offline Learning

Build the model using periodic batch jobs (e.g., Every 4 hours) and refresh the new model onto the scoring pipeline.

Effective Post-production Monitoring

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

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

Data Exploration

Apply appropriate unsupervised algorithms to uncover hidden structures in the data.

On-the-fly charting capability to explore co-relation between different variables.

 

Data Exploration

Apply appropriate unsupervised algorithms to uncover hidden structures in the data.

On-the-fly charting capability to explore co-relation between different variables.

 

Data Visualization

This data science platform offers powerful data visualization tools and automated visual and statistical reports that help you:

  • Immediately identify features with the most impact on your prediction with variable importance
  • Quickly understand complex feature interactions and analyse coefficients
  • Help you interpret the clusters resulting from un-supervised real-time machine learning

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StreamAnalytix Platform Overview

StreamAnalytix™ enables enterprises to analyse and respond to events in real-time at big data scale using stream processing and machine learning….

Impetus at DataWorks Summit 2018: How a leading bank leveraged Apache Spark and StreamAnalytix to rapidly rebuild their insider threat detection application

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