Container & Edge Orchestration

О курсе

Running AI workloads reliably across data centers, Kubernetes clusters, and resource-constrained edge devices requires more than container basics. This course builds the skills to deploy, optimize, and manage containerized inference services from cloud to edge at scale. You'll package models with Helm, validate rollouts, and trace requests with OpenTelemetry and Jaeger to fix latency outliers. You'll build minimal Docker images, configure edge inference using Azure IoT Edge and ONNX Runtime, implement resilient gRPC communication, and execute zero-downtime Blue-Green deployments on AKS. By the end of this course, you'll be able to define deployment standards for containerized inference services, set container optimization targets, select edge inference architectures and communication protocols, validate resilience under injected failures with Chaos Mesh, and own rollback criteria and SLI thresholds for cloud and edge deployments. This course is designed for platform engineers extending container orchestration skills to edge computing and AI inference workloads. Familiarity with Docker and basic Kubernetes concepts is expected.

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Microsoft

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