WBF AcademyWBF Academy
Cloud Native Mastery
Intermediate Cloud Infrastructure

Cloud Native Mastery

Master containers, Kubernetes, and Infrastructure as Code from first principles, then deploy production agentic AI workloads to AWS, Azure, and GCP. Covers Docker, K8s primitives, Terraform, EKS/AKS/GKE, and the patterns for shipping LLM agents at scale.

5 modules 15 lessons 7 h 350 questions
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Course content

1 Containers 3 lessons · 23 min
1 What Is a Container, Really 7:12 2 Building Container Images 7:40 3 Registries and Image Distribution 8:00
Practice · 0 of 70 questions Practice →
2 Kubernetes Core 3 lessons · 22 min
4 Kubernetes Workloads 7:03 5 Networking and Configuration 7:26 6 Control Plane Internals 7:51
Practice · 0 of 70 questions Practice →
3 IaC with Terraform 3 lessons · 22 min
7 Terraform Fundamentals 6:53 8 State and Collaboration 7:30 9 Modules, Patterns, and OpenTofu 7:32
Practice · 0 of 70 questions Practice →
4 Multi-Cloud Kubernetes 3 lessons · 24 min
10 Amazon EKS in Production 8:07 11 Azure AKS in Production 7:55 12 Google GKE in Production 8:26
Practice · 0 of 70 questions Practice →
5 Agentic AI Deployment 3 lessons · 26 min
13 Serving LLM Agents on Kubernetes 8:18 14 Autoscaling AI Workloads 8:23 15 Cloud-Native AI Integrations 9:33
Practice · 0 of 70 questions Practice →

Tips

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Observability Observability for LLMs: track tokens Latency and errors are not enough AWS Call Bedrock Claude via IRSA, no keys Pod identity > long-lived API tokens AI-Workloads Prompt cache sidecar in front of the model Hash the prompt, skip the GPU AI-Workloads Warm models before flipping ready Cold-start latency is a real outage