Decision intelligence firm Aera Technology has announced new machine learning and developer capabilities for its Aera Decision Cloud, a data science platform that digitizes, augments and automates supply chain decision making.
The new features advance and scale Aera Cognitive Skills — the platform’s pre-built set of comprehensive decision-making capabilities for demand forecasting, planning, inventory, logistics, procurement, finance, revenue and more. The release also delivers enhancements for data engineers, data scientists and developers that optimize their experience in Aera Developer, the platform’s integrated development environment (IDE) for creating or modifying Skills.
Highlights include:
Enhancements to Aera Cortex, the data science engine inside the Aera Cognitive Operating System, to improve the user experience, including:
New Aera Notebook: Jupyter Notebook open interface provides the ideal environment to easily integrate data science projects and other custom code using Python and R, into Decision Intelligence flows, right from the Aera Decision Cloud platform.
New AutoML: Machine learning options enable development of ML decision models — without requiring expertise in model creation, tuning, and deployment.
New Model Performance and Versioning: New enhancements automate monitoring, deployment, and versioning of machine learning models to make it much easier to operationalize ML models and scale across the entire enterprise.
Advanced intelligence capabilities to provide a deeper understanding of relationships and effectiveness of decisions, including:
New Graph Explorer: Advanced graph capabilities to enable data relationships to be used effectively in the decision-making process.
New Confidence Score: Confidence score framework, which learns from past, similar recommendations and outcomes, helps determine the decisions that can be automated.
New Action Item Node: Addition of a node within the Aera Developer environment to simplify decision management through the ability to more easily create, update, close, and access recommendations.
