From data management to model development and deployment, everyone works in the same integrated environment. Let the algorithms do the work.
Superior performance from massive parallel processing and the feature-rich building blocks for machine learning pipelines let you explore and compare multiple approaches rapidly.
Reduce latency between
data and decision
To enhance collaborative understanding, the solution provides all users with business-friendly annotations within each node describing what methods are being run, as well as information about the methods, results and interpretation.
MACHINE LEARNING CAPABILITIES
services & capabilities
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.
Deep learning with Python & ONNX support
Enables Python users to access high-level APIs for deep learning functionalities within Jupyter notebooks via the 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.