MicroAI AtomML is an Edge-Native AI platform that lives directly on the MCU or MPU of a device or machine. AtomML provides deep observability into the performance, health, and security of IT and OT assets. Operational Excellence at the endpoint.
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.
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.
See DemoFully 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.
See DemoReal-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.
See DemoEmbedded 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.
See DemoThe 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.
See DemoHigh-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.
See DemoThrough 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.
See DemoAtomML 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:
