Data Mining and Machine Learning
Our Cloud First AI platform or Platforms offered though our partners support the end-to-end data mining and machine learning process with a comprehensive visual – and programming – interface. Empowers analytics team members of all skill levels with a simple, powerful and automated way to handle all tasks in the analytics life cycle.
Automated insights & interpretability
Automatically generates insights, including summary reports about the project, champion models and challenger models. Simple language from embedded natural language generation facilitates report interpretation and reduces the learning curve for business analysts.
Automated feature engineering & modeling
Saves time and improves analytics team productivity. Automated feature engineering selects the best set of features for modeling by ranking them to indicate their importance in transforming your data. Visual pipelines are dynamically generated from your data, yet are editable to remain as a white box model.
Public API for automated modeling
Lets you take advantage of the public API for automated modeling for end-to-end model development and deployment simply by choosing the automation option. Or use our API to build and deploy your own custom predictive modeling applications.
Provides best practices templates that enable a quick, consistent start to building models, and ensures consistency among the analytics team. Analytical capabilities include clustering, different types of regression, random forest, gradient boosting models, support vector machines, natural language processing, topic detection, etc.
Deep learning with Python & ONNX support
Enables Python users to access high-level APIs for deep learning functionalities within Jupyter notebooks via Deep Learning with Python (DLPy) open source package on GitHub. DLPy supports the Open Neural Network Exchange (ONNX) for easily moving models between frameworks.
Integrated data preparation, exploration & feature engineering
Lets data engineers quickly build and run transformations, augment data and join data within the integrated visual pipeline of activities using a drag-and-drop interface. Performs all actions in memory to maintain data structure consistency.
Highly scalable in-memory analytical processing
Enables concurrent access to data in memory in a secure, multiuser environment. Distributes data and analytical workload operations across nodes – in parallel – multithreaded on each node for very fast speeds.
Computer vision & biomedical imaging
Lets you acquire and analyze images with model deployment on server, edge or mobile. Supports the end-to-end flow for analyzing biomedical images, including annotating images.
Code in your language of choice
Lets modelers and data scientists access Cloud First capabilities from their preferred coding environment – Python, R, Java or Lua – and add the power of our cloud p to other applications with REST APIs.
Cloud- & container-ready
Deploy in the cloud (private and public) or hybrid on-site and cloud. Available as a predefined Docker container with recipes available on GitHub.