MicroAI Use Cases#
MicroAI supports a variety of different use cases depending on the user’s needs and device capabilities. Since our AIengine is designed to run on low-power and low-memory devices such as MCUs or MPUs, you can choose your use case depending on those parameters. To see the complete how-to for each use case, please select from the options below. MicroAI AtomML is designed for MCUs and MicroAI AtomML+™ is designed for MPUs or more powerful architecture like the CPU in your computer. Our AI engine can also be used for cybersecurity; it uses the same technology as AtomML+™ but monitors different hardware channels to recognize and predict cyber threats.
Predictive Analytics – More than Dashboards#
MicroAI’s AI/ML edge and endpoint solutions embed and train predictive algorithms directly into IT and OT device and machine endpoints. This personalized technology provides developers, operators, and engineers with the predictive analytics required to achieve higher levels of operational efficiency and OEE. A synopsis of how this works:
Asset Data Acquisition#
Data is leveraged from a variety of IT and OT devices and machines. MicroAI’s technology is agnostic to sensor values and types. It creates a multi-variant model that utilizes AI inference analysis to generate a wide range of predictive analytics.
Device and machine performance data is synthesized and analyzed locally—in real-time. Sensitive data is also stored locally, minimizing the amount of data that is transferred to the cloud. Predictive analytics data latency is eliminated, and exposure of sensitive data is reduced.
Presentation of real-time asset performance data via user-friendly, customizable, drag and drop dashboards. Data is customized to meet the specific requirements of various operational and business stakeholders.
MicroAI utilizes multidimensional behavioral algorithms to produce recursive analysis, training, and processing. This enables a continuous evolution of the Edge-native AI model that takes place directly at the endpoint or the edge.
Analytics and Alerts#
AI at the extreme edge provides deeper, more intimate insights into asset health and performance that have not been available with traditional AI solutions. Asset optimization is achieved via predictive insights instead of assumptions.
Next-Generation Cyber Protection#
Yesterday’s cybersecurity is not effective against today’s increasingly sophisticated cyber-criminal. The threat landscape is constantly evolving, driven by malicious actors with a fully weaponized arsenal of tools and methodologies. MicroAI takes endpoint security to the next level of effectiveness by combining several AI-enabled security elements into a single, localized, cost-effective solution.
The Escalating Threat#
Global cyber-attacks are increasing at a rate of ~300% year over year. The sheer volume of attacks overwhelms the personnel and existing tools responsible for cyber protection.
Device Power/Memory Limitations
Many IT and OT edge devices operate on low power and limited memory. These devices are less adaptable to traditional security measures and more vulnerable to attack.
Many IT and OT asset ecosystems contain devices and machines that have inconsistent connectivity. Connectivity disruptions increase the risk of cyber intrusion.
Inability to Predict Threats
Most legacy security tools are purely reactive. Asset stakeholders have no ability to predict potential cyber threats or to take preventive action.
Crippling Ransomware Attacks
Ransomware attacks can result in the temporary or permanent loss of sensitive data. Depending on the scope of the attack, a single asset, an entire factory, or a chain of interconnected factories can be disabled.
High Cost of Data Processing
Heavy reliance on cloud-based data processing adds significant cost to security programs. Configuration costs can also be prohibitive.
MicroAI AtomML™ is an Edge-native AI, self-correcting, semi-supervised learning engine that aggregates data from internal device sensors, to tune itself to create a behavioral profile of the asset, which then detects and acts upon abnormal behavior - delivering performance improvements and security enhancements to any device. MicroAI AtomML™ brings big infrastructure intelligence into a single piece of equipment or device. Unlike traditional AI-driven asset management solutions that rely on the cloud, MicroAI AtomML™ is deployed directly onto smart devices and sensors. MicroAI AtomML™ operates within the small environment of the device itself, providing a more efficient method for asset analytics and generating real-time alerts. AtomML optimizes asset performance while simultaneously enhancing security oversight. MicroAI AtomML™ brings an intimate, local approach to asset management for producing a host of operational efficiencies.
The AtomML™ algorithm is firmware that is designed to run on memory-constrained devices. Memory-constrained devices are typically MCUs which are designed and built to be very low cost. The MCU can be a separate processor dedicated to AtomML, or the same MCU used by a system to control operations.
MicroAI AtomML+™ is an Edge-native AI algorithm primarily focused on anomaly detection. It is designed to run on MPU-based edge devices. This algorithm allows for more robust inferencing due to using a device powerful enough to monitor multiple assets at once. 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. Due to the increased power of the hardware on the MPU more input channels can be added and allow for multi-asset monitoring. MicroAI AtomML+™ does both the training and inferencing on the edge reducing cloud storage costs and latency issues common with other machine learning processes. It is agnostic to data source so the possibilities of assets that can be monitored is only limited by creativity (if the data is continuous, time-series data). MicroAI AtomML+™ optimizes asset performance and enhances security oversight bringing a robust, multi-asset, and local approach to device management.
MicroAI Security is an Edge-native AI platform that embeds and trains advanced security algorithms directly into a device, machine, or process. Creating endpoint cyber protection.
MicroAI Network Quality of Service (NQoS)#
MicroAI Network Quality of Service (NQoS) is an edge-native AI that embeds and trains on the network channels and creates insights into the quality of the cellular network for your IOT devices. This provides increased value into how connected your device is and if the device is experiencing any trends towards disconnectivity.
MicroAI AIStudio will allow you to stream live data from your edge device or upload a csv file with historical data and test feature engineering functions, tune parameters and test different outputs against a confusion matrix to customize your MicroAI solution so that it fits your exact needs.