MicroAI AtomML+™ is an Edge-native AI algorithm primarily focused on anomaly detection designed to run on MPU Edge Devices.
This algorithm allows for more robust inferencing than MicroAI AtomML™ due to being designed for more powerful edge devices.
MicroAI AtomML+™ leverages the power of edge MPUs to:
Monitor, Detect, and Predict faults for multiple assets simultaneously
Find Rootcauses for device fault and failure
Enhances security and oversight bringing a robust, multi-asset, and local approach to device management
Can output a corrective signal to bring your system-state back to normal
Reduces cloud storage and computing costs common with other machine learning processing
The overall result is still the same as MicroAI AtomML™ in that it aggregates data, creates a behavioral profile of the asset, then it detects and acts upon abnormal behavior.
Flexibility, Scalability, Cost, Speed & Agility#
With MicroAI AtomML+™, you can enjoy:
FLEXIBILITY & SCALABILITY: MicroAI AtomML+™ can be embedded on a group of devices and can monitor each device individually or as an entire asset ecosystem. This creates a network of intelligence.
COST: MicroAI AtomML+™ allows for machine learning training and processing to take place directly on MPUs. This allows data to be processed on the edge, reducing data storage costs since it will only send data that is important to asset health.
SPEED & AGILITY: since the data is processed on the edge, MicroAI AtomML+™ sends updates regarding your asset ecosystem in real-time, reducing costly downtimes.
MicroAI AtomML+™ monitors multiple assets in a group on the Edge: This allows whole assembly lines or devices working together to be observed as a group and a health score can be generated for each asset individually. This allows for more robust observability while still taking advantage of the low latency and low data requirements unique to MicroAI AtomML™.
MicroAI AtomML+™ Demo#
Watch the MicroAI AtomML+™ Demo on YouTube: