MicroAI AtomML+ is an agentless machine learning platform for optimization of IT and OT asset Ecosystems. AtomML+ brings centralized intelligence to groups of devices and machines to deliver optimized performance, enhanced cyber-security, and reduced cost.
Building upon the capabilities of MicroAI AtomML, MicroAI AtomML+ provides edge-based intelligence and observability across multiple devices and machines within an IT or OT asset ecosystem. AtomML+ is a proprietary machine learning (ML) platform that aggregates data from device and machine sensors to create behavioral profiles and health scores at the group level and the individual asset level. Key Features include:
AtomML+ is an agentless technology that collects and streams asset data from multiple endpoints into one centralized location.
Machine learning that is designed specifically to run on devices and machines with limited memory and compute capacity such as Cortex A class MPUs. It can also run on other standard architectures, including: X86, RISC, MIPS, etc.
AtomML+ has the unique ability to provide real-time performance observability into an individual machine or into large groups of assets within the IT or OT environment.
Multivariate analysis is performed across large volumes of input channels to generate predictive analytics related to performance and maintenance.
AtomML+ processes data at the edge, vs in the cloud, reducing overall data handling cost by 70 to 80% and eliminating problems associated with data latency.
AtomML is designed to be embedded into a single IT or OT asset while AtomML+ provides the inbuilt scalability to be applied to an ecosystem of assets. Comparative features and applications include:
Embedded ML engine for individual asset optimization
Edge-based ML engine for optimization of groups of assets
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+ enables deep observability and multi-spectrum predictive analytics within complex IT and OT infrastructures. AtomML+ provides AI / ML derived actionable insights, at the machine, factory, and enterprise levels. Asset ecosystem optimization advantages that include:
Data produced by groups of assets is managed from a centralized location. This improves the speed and reliability of data insights and actionable alerts.
Depending on configuration, AtomML+ can either generate health scores for each individual asset, or a single health score for the entire IT or OT asset ecosystem.
AtomML+ is an agentless-based platform that requires no external software processes and is less reliant on development of customized application code. AtomML+ can be deployed in a wide range of IT and OT ecosystems.
Operational excellence requires not only knowledge of what is happening now, but insights into potential future developments. AtomML+ provides the deep learning that is required for true predictive analytics and insights. Moving from reactive to predictive asset management.
AtomML+ trains advanced security algorithms directly into the devices, machines, or processes within the IT or OT infrastructure. AtomML learns the normal state of asset behavior and provides early-stage detection of profile deviations caused by cyber intrusion. Comprehensive cyber-security that delivers:
