MicroAI AtomML is an Edge-Native AI, self-correcting, semi-supervised learning engine that aggregates data from device and machine sensors to create a behavioral profile of the asset and then actively monitors for abnormal performance and cyber-security intrusions. Advantages to this endpoint approach include:
AtomML processes data at the edge, vs in the cloud, reducing overall data handling cost by 70 to 80%.
Predictive algorithms minimize maintenance costs while optimizing asset health scores, uptimes, and productivity.
AtomML learns the normal state of a device or machine and actively monitors for abnormal behavior induced by cyber-attack.
By processing asset data locally, AtomML allows for rapid data sampling rates for real-time monitoring of asset performance without the need for transmission of data to the cloud.
AtomML embeds and trains advanced security algorithms directly into a device, machine, or process. AtomML learns the normal state of device behavior and provides early-stage detection of profile deviations caused by cyber intrusion. Edge-Native AI security that delivers:
AtomML embeds security learning and protocols that are customized for the specific device or machine.
Processing critical data at the endpoint eliminates security risks associated with cloud data transfer and storage.
Endpoint security provides more precise analysis of current asset state as well as actionable predictive analytics.
AtomML provides asset cyber protection that is more hardened, more predictive, more rapid, and less costly than other solutions available today.
Many Industry 4.0 initiatives are geared toward improving OEE (overall equipment effectiveness). The manufacturing and industrial automation segments have struggled to surpass the 70% OEE mark. AtomML is the Industry 4.0 solution to improved OEE.
AtomML has a tiny footprint, is hardware agnostic, is common code based, and can be deployed onto virtually any type of device or machine. AtomML requires no data labelling or expensive pre-training. AtomML can be deployed in several ways, including: