AI curriculum
Course outline for Cloud-Native Engineering
Containerized applications need explicit configuration and operational behavior. Package and deploy a small service while inspecting discovery, scaling, and observability controls.
About Cloud-Native Engineering
Containerized applications need explicit configuration and operational behavior. Package and deploy a small service while inspecting discovery, scaling, and observability controls.
Cloud-Native Engineering Course Objectives
- Package a service in a container.
- Define Kubernetes configuration and service boundaries.
- Inspect scaling and failure behavior using telemetry.
Pre-requisites
- Basic Linux and application deployment skills.
- Familiarity with networking and command-line tools.
Lab Setup
- Computer with Docker or a compatible container runtime and a local Kubernetes cluster.
- Use synthetic data and dummy secrets; no cloud account or production cluster is required.
Detailed Course Outline
Proposed modulesModule 1: Packaging and orchestration
- Containers
- Docker
- Kubernetes
Practical outcome: Package a sample service and define its workload configuration.
Module 2: Runtime configuration
- Service discovery
- Configuration
- Secrets
Practical outcome: Separate configuration from code and use dummy secret values safely.
Module 3: Operational behavior
- Scaling
- Networking
- Observability
Practical outcome: Inspect service connectivity and scaling signals.
Practical exercise
- Deploy a sample service locally and investigate a configuration or connectivity failure.
How we train
Contact us for full course details, including duration, delivery options and lab requirements.
Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.
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