Live data is leveraged from a variety of devices, machines, and networks. MicroAI’s technology is agnostic to sensor values and types, creating a multi-variant model that utilizes AI inference analysis to generate a wide range of analytics.
Auto - Tuning
Fully automatic tuning of the AI model(s) to be deployed. Multidimensional behavioral algorithms produce recursive analysis, training, and processing. This enables a continuous evolution of the AI model that takes place directly on the endpoint.
Real-time, on-demand, health scores provide continuous observability into the health, performance, and security of connected assets. Stakeholders and operators can fast-track health assessments and to identify recurring problems based on historical data and predictive insights.
Embedded ML algorithms learn the normal operating behavior of an individual machine or a group of machines. Deep federated learning provides the accurate baselines required to rapidly detect performance anomalies of any size or duration.
The embedding and training of intelligent workflows automate the process of performance alert notifications to ensure accurate dissemination of critical information. Alert routines can be customized to accommodate specific ecosystem configurations and requirements.
High-speed processing of historical asset performance data enables rapid detection of historical patterns as well as analysis of relationships between complex variables impacting the performance of a machine or machine group. Root cause identification accuracy is improved, leading to faster recovery and reduced downtime.
Through accurate identification of root cause, the algorithms will identify effective corrective actions to be implemented. Once implemented, the AI engine provides real-time impact assessments and self-tunes for maximum performance.