RubiML
RubiML offers Predictive Analytics functionality, which includes several ready to use algorithms for needs such as Classification, Regression, Association Rules, and Clustering. It has interactive data exploration and model analysis features that make it easy to build predictive models without requiring in-depth knowledge of coding. It also offers AutoML, a unique feature that automates selecting, testing, and recommending algorithm. It also creates a pre-built model for you so you can just publish it readily!

Advantages

Boost analytical productivity with RubiML. Find creative answers to the most complex problems faster. RubiML is an open, scalable, advanced predictive analytics environment that:
  • Enables significant performance gains by eliminating barriers caused by data size, data diversity, limited analytical know-how, and computational bottlenecks
  • Analyze diverse types of data, with unseen granularity. Delve deeper into data than ever before, exploring anywhere and everywhere to unearth opportunities that previously remained hidden
  • With a single, integrated environment that facilitates all steps necessary to convert raw data into valuable insights
  • Avail descriptive, inferential statistics and predictive modeling techniques at your fingertips to create, compare and refine models on the fly to achieve optimal results
  • Foster collaboration in an easy-to-use, intuitive, and shared environment where multiple users can simultaneously analyze large amounts of data
  • Wizard-like interface for business users to quickly build simple models/predictors
  • Rich library of over 100+ pre-built machine learning algorithms and functions to build the best model for any given use case.
  • Automatic feature engineering, generation, and selection to use any kind of data in your models.
  • Optimize your model hyperparameters using various cross-validation techniques
  • Get instant visual insights from your model (variables importance, features interactions or parameters), and assess model’s performance through detailed matrices
  • Snapshot functionality to help create backup/versioning of model development and the ability to roll back to a stable version during experimentation
  • Support for Keras, Tensorflow backend, integrate with Tensorboard, and scale model training using GPU
  • Can be scaled vertically and horizontally; autoscaling to cater to sudden workloads