Blueprints in the Kubernetes-Native Infrastructure for Edge family leverage the best-practices and tools from the Kubernetes community to declaratively and consistently manage edge computing stacks from the infrastructure up to the workloads.
They support both containerized and VM-based applications on a common infrastructure and lifecycle-manage these applications using the Operator framework. Building on the Kubernetes Machine API allows users to deploy them consistently anywhere, from VMs in developer environments to bare metal production environments and from on-prem to public cloud.
This “Industrial Edge” blueprint demonstrates using Kubernetes, ACM, AMQ Streams, OpenDataHub, and other projects the OpenShift ecosystem to address a common edge computing use case commonly found in manufacturing: Machine inference-based anomaly detection on metric time-series sensor data at the edge, with a central data lake and ML model retraining.
Key Features:
- Managing edge computing clusters from a central management hub by using Advanced Cluster Manager
- GitOps based application deployment with ArgoCD
- Cloud Native CI/CD Pipelines with Tekton
- Event streaming from edge to core with Kafka AMQ Streams and Mirror Maker
- Machine learning as a data scientist with Jupyter Notebook.