Observability
Deep asset observability at the edge. Using the latest in embedded and edge AI capabilities, MicroAI™ brings real-time observability to any type of IT or OT device or machine. From low-powered edge devices to high-powered servers, MicroAI delivers deep insights into asset performance and security at costs that are up to 20x lower than legacy solutions.
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Deep Observability – Beyond Simple Monitoring
Observability is about generating actionable insights from the data generated by assets within an IT or OT infrastructure, insights that enhance the operation’s ability to maximize OEE (overall equipment effectiveness) and security. Observability goes well beyond static monitoring and logging.
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Dynamic Learning
Learning the normal behavior of an individual asset or a group of assets.
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Endpoint Monitoring
Utilizing embedded machine learning (ML) algorithms to provide deep insights into real-time performance and health of assets.
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Recursive Analysis
Recursive analytics that produce predictive insights into future asset health and maintenance requirements.
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Insight Mining and Distribution
Embedded intelligent workflows that deliver insights and alerts to designated stakeholders.
The Observability Gap
Organizations are plagued by a host of problems resulting from legacy solutions that watch but that do not observe. This observability shortfall produces numerous barriers to operational excellence.
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Connectivity Issues:
IT and OT assets are either not connected or have unreliable connectivity.
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Lack of Edge Processing:
Inability to process data at the edge means that all data must be transmitted to the cloud for expensive processing.
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Siloed Monitoring:
Silo-bound monitoring limits the ability to take maximum advantage of data that is produced.
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Lack of Predictive Intelligence:
Legacy monitoring solutions do not produce the intelligence needed to develop predictive analytics.
A typical edge device or compute unit can produce 100 MB to 1 GB of telemetric data daily. The high expense of data transmission can be a prohibitive factor. Scaling and processing high volumes of data is increasingly costly for operations that rely on cloud-based infrastructures. Challenges include:
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Overwhelming Volume
Traditional monitoring solutions are not equipped to manage large volumes of asset data, requiring additional external—costly— support.
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Lack of Discernability
No ability to quickly discern critical data from non-critical data results in costly processing inefficiencies.
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Cloud Dependance
Heavy reliance on cloud infrastructures creates prohibitive cost burdens due to data latency, management inefficiencies, and data transmission.
Observability – A New Paradigm
MicroAI is redefining asset observability by moving observation closer to the asset and by providing the intelligence to maximize that observability. MicroAI is the only technology that provides local AI model training and observability. Technology that provides:
- Observability embedded directly into the MCU/MPU of an asset
- Consumable AI outputs for asset observability applications
- Continuous model adaption and training based on real-time asset conditions
- Model training and inferencing that occurs at the edge
- Predictive analytics that enable predictive maintenance and health scores
- Less reliance on cloud support for data processing
- Tiny footprint that requires no labeled data
- Simple integration into an existing IT and OT ecosystem
Operation Excellence via Observability
MicroAI offers a powerful AL/ML platform that covers the entire spectrum of enterprise infrastructure observability. MicroAI’s breakthrough technology enables the observability required to maximize OEE and attain new levels of operational excellence. Differentiators that include:
- Seamless and secure ingestion of data from a wide variety of IT and OT devices and machines (sensors, robots, field assets, network equipment, VMs, etc.)
- A centralized visualization engine that provides continuous observability into the status of connected devices and machines
- The embedding and training of intelligent workflows that automate the process of performance alert notifications and implementation of rapid threat mitigations
- Predictive analytics produce actionable insights into future conditions. Deeper observability that enables a transition from reactive to predictive asset management
- The embedding and training of intelligent workflows that automate the process of responding to data produced by deep observability
- Embedded and edge intelligence that is collected, synthesized, and analyzed locally. The amount of cloud support is reduced, resulting in significant costs savings
Interested in how MicroAI can benefit you?
MicroAI AtomML brings big infrastructure intelligence down into a single piece of equipment or device.
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