AtomML™
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.
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Asset-Specific Machine Learning
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:
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Lower Cost
AtomML processes data at the edge, vs in the cloud, reducing overall data handling cost by 70 to 80%.
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Predictive Analytics
Predictive algorithms minimize maintenance costs while optimizing asset health scores, uptimes, and productivity.
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Enhanced Security
AtomML learns the normal state of a device or machine and actively monitors for abnormal behavior induced by cyber-attack.
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Speed and Agility
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.
Our Tech: Closed-Loop Asset Observability
- I/O Layer
- Auto - Tuning
- Health Scores
- Fault Detection
- Alerts
- Root Cause
- Corrective Action
I/O Layer
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.
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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.
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Health Scores
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.
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Fault Detection
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.
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Alerts
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.
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Root Cause
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.
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Corrective Action
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.
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Customized Asset Observability
AtomML embeds personalized intelligence into individual devices and machines within the asset ecosystem. Operating within the asset itself, AtomML provides deeper–and more efficient– observability into the performance, health, and security of the device or machine. Benefits of this endpoint visibility include:
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Small Footprint
AtomML is small enough to live, train and inference on a micro-controller (MCU) or micro-processor (MPU) eliminating the need for extraneous hardware and minimizing cloud dependance.
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Endpoint Intelligence
Proprietary algorithms analyze times-series data from machine and device sensors to deliver deep insights into the behavior of critical assets.
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Predictive Maintenance
AtomML provides the learning and asset observability required to evolve from planned (inefficient) maintenance routines to predictive routines that are more productive and less disruptive.
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Rapid Alert and Mitigation
AtomML utilizes multidimensional behavioral algorithms to produce recursive analysis, training, and processing, providing real-time performance alerts and workflow-enabled notifications and mitigations.
Asset-Centric Cyber Security
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:
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Asset-Specific Security Insights
AtomML embeds security learning and protocols that are customized for the specific device or machine.
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Local Monitoring and Processing
Processing critical data at the endpoint eliminates security risks associated with cloud data transfer and storage.
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Improved Precision
Endpoint security provides more precise analysis of current asset state as well as actionable predictive analytics.
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More Robust and Less Costly
AtomML provides asset cyber protection that is more hardened, more predictive, more rapid, and less costly than other solutions available today.
MicroAI AtomML™ brings big infrastructure intelligence down into a single piece of equipment or device.
Improved OEE
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.
- A 15% improvement in OEE can equate to a 17% increase in productivity. An operation producing $60M worth of products can increase their output to ~ $70M.
- Improved OEE equates directly to a reduction in asset maintenance costs. Unnecessary maintenance is eliminated via the implementation of predictive maintenance.
- Higher OEE scores translate to improved quality of the products being produced. Machine and device performance are more reliable and more predictable.
- Production costs are reduced. This results in improved product pricing as well as healthier bottom lines.
Rapid and Cost-Effective Deployment
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:
Interested in how MicroAI can benefit you?
MicroAI AtomML brings big infrastructure intelligence down into a single piece of equipment or device.
See Demo