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• Edge native applications are designed to process data closer to where it is generated, reducing latency and security risks associated with sending data to the Cloud
• Nine principles for Edge native applications include resource and deviceware, protocol management, and scalable management
• Examples of Edge native applications include factory assembly lines and predictive maintenance
• Data security is a key concern for Edge native applications, with encryption and secure key management being important practices
• Edge native applications can also inform Cloud native applications, particularly in terms of specialized hardware and management techniques

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• Workloads are moving from data centers to the edge, and Kubernetes has been adopted to run these workloads.
• The challenge is to secure these workloads and manage certificates and renewals at scale.
• Cert-manager and SPIFFE can be used to manage security at the edge and distribute trust.
• The talk demonstrates how to provision and renew certificates for both ingress and mTLS use cases using cert-manager on a Raspberry Pi.

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• The challenge of deploying applications at the edge is to have a consistent operational and management model across different environments
• Edge native applications need to take into account the different attributes of the edge, such as data handling and networking
• Efficient data handling at the edge is crucial, with the need to balance processing at the edge and sending training bits back to the central model
• Kubernetes and OpenShift can provide consistency across different environments, with the use of single node OpenShift allowing for deployment in disconnected environments
• The use of ML/AI, 5G networking, and hardware acceleration are improving the evolution of edge technology

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• KubeEdge is a Kubernetes-based project for edge computing
• The stability test shows that KubeEdge can support 100 nodes and manage one million deployed pods
• The test results show impressive latency performance for both mutating and read-only API calls
• The presentation mentions plans to improve security and device mappers, as well as support for cross-submarine communications and edge clusters
• The KubeEdge project is collaborating with telecom companies and contributing to white papers on 5G multi-access edge computing

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• Developers should be responsible and focus on doing good when working with AI
• AI has the potential to benefit society, such as in home robots that provide companionship and peace of mind
• Ford is providing a software development kit for Astro, their home robot, to university partners to accelerate innovation
• Working in AI is important for society and provides opportunities for learning and growth

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• Kubernetes can be deployed on edge nodes transparently to developers
• Active-active deployment can prevent connection loss between cloud and edge
• IoT cases involve deploying apps on edge nodes
• Performance testing includes latency, throughput, scalability, CPU usage, and memory usage
• Kubernetes scalability is multi-dimensional and requires careful configuration
• Decentralized security and network policy are being researched for edge nodes

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• The Sedna project is an AI toolkit for age cloud collaboration and synergy mechanism for AI workloads
• The project simplifies incremental learning and enhances the federation of federated learning to support more scenarios for the robotic SIG
• The Sedna project helps achieve joint influencing for day-to-day AI workload and simplifies the model training upgrade iteration
• The project focuses on API definition and the reference architecture as well as implementation relevant to the robotics ecosystem
• The Sedna project may focus on containerizing some of the software including the iOS and the engageable
• The project is used in the world's longest process C bridge to monitor metrics of the bridge itself and track traffic to generate emergency alerts
• The Sedna project aims to achieve age cloud collaborative architecture or robot cloud collaborative architecture