Lynx’s bare metal support for Google Anthos secures Kubernetes in the cloud

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When it comes to mission-critical applications, the biggest challenge is to securely deploy artificial intelligence (AI) from the cloud to the edge. Once you have trained the AI ​​and machine learning models in the cloud, the operational question is how to deploy these cloud-based workloads at the edge and ensure on-premises system security.

That’s what the latest announcement from Lynx Software Technologies (Lynx) hopes to address. The company announced that its MOSA.ic platform now supports the deployment of Google Anthos Bare Metal on the mission critical edge. The result is the ability to deliver containerized software services from the cloud, such as B. the Google Cloud Visual Inspection AI service, which offers a validated solution for secure, video-based quality inspection in industrial and energy plants.

Lynx said it ensures the three functions – image capture (camera), insight via inference engine (Google Anthos) and action with a surveillance controller – are housed entirely in a sandbox, with the option of secure one-way connections (data diode) between them .

Through the partnership, the new solution enables:

  • Easily deploy real-time image capture to devices such as cameras on production floors.
  • Inference models created by Google Cloud Visual Inspection AI that generate the insights.
  • A higher-level control that connects to the Manufacturing Execution System (MES) and converts findings into actions.

In addition, Lynx’s technology provides immutable isolation and unbreakable security to provide plant or facility managers with confidence that the solution meets operational technology (OT) security requirements.

In an interview with embedded.com, Pavan Singh, Vice President of Product Management at Lynx, said: “To date, there has been a lot of focus on building AI models, but very little maturity in deploying AI and ML at the operational level on-premises System is immune.” With support for Google Anthos Bare Metal, this now means an entire Kubernetes cluster can run locally in just one hardware system at the edge, with Lynx-enabled virtual air gapping providing isolation between the different parts of the system .

“As a business-critical edge provider, we are excited to announce support for Google Anthos Bare Metal and Google Cloud Visual Inspection AI. With the new solution we are bringing to market together, any containerized service can now be deployed at the mission-critical edge without compromising security or performance. This partnership also marks an important step in our growing industrial ecosystem.”

Singh highlighted the benefits of providing this secure divider and Lynx’s experience in providing isolation, saying: “The divider is important. We have been dealing with the case of separation cores for about 10 years. The advantage of our work is that we can see the hardware and also run code on the bare metal.”

A new video-based quality solution enabled by the partnership enables industrial and energy facility managers to securely deploy AI and other cloud-based workloads at the mission-critical edge. (Image: Lynx Software Technologies)

According to Lynx, for industrial and energy companies already suffering from supply chain disruptions, labor shortages and more, this video-based quality system plays an important role in improving performance and the quality of output while mitigating safety risks. Efficient visual inspection can reduce errors by up to 10x, prevent defective parts from being shipped and gain insight into the root cause of errors to optimize processes. Singh said the visual inspection solution can open up more possibilities, with use cases such as part defect detection, weld inspection, PCB inspection and silicon wafer defect analysis.

The solution runs on an Advantech MIC770, however the Lynx MOSA.ic solution can run on various Intel and ARM processors. This flexibility means that different types of solutions can be managed locally/on-premises while taking full advantage of cloud solutions.


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