AI-400
Cloud-Native AI — Learn Dapr, Docker & Kubernetes with AIDD
Build cloud-native infrastructure for intelligent agent systems. Using AI-Driven Development (AIDD), you’ll learn Docker, Kubernetes, and Dapr to design and deploy production-ready AI agents with observability, scalability, and cloud-agnostic flexibility — laying the foundation for autonomous AI at scale.
Available Sections:
Details
The way we build and run AI is changing. It’s no longer just about running agents on your laptop—it’s about deploying intelligent systems that live and scale in the cloud. Cloud-Native AI gives you the skills to do exactly that: take your AI projects from local experiments to production-grade systems.
In this course, you’ll learn how to build cloud infrastructure for AI agents using Specification-Driven Development and AI-Driven Development (AIDD). You’ll start with FastAPI and Docker, packaging your applications into portable containers. Then move to Kubernetes, learning how to run and manage those containers at scale. Finally, you’ll explore Dapr, the layer that makes your AI systems cloud-agnostic, with built-in support for state management, pub/sub messaging, and service communication. Each step is practical and hands-on.
You’ll containerize real AI apps, deploy agent systems on Kubernetes, and use Dapr to connect everything together—without locking yourself to a single cloud provider. By the end, you’ll know how to build, deploy, and manage AI agents that scale automatically, recover gracefully, and run anywhere. This course turns developers into cloud thinkers—people who don’t just code AI, but design the systems that make AI work in the real world.
Key Learning Modules
Module 1Foundations: Cloud Native Infrastructure for AI
Master Context Engineering to structure AI collaboration for infrastructure design. Partner with Claude Code to understand containerization (Docker), orchestration (Kubernetes), and cloud-agnostic abstractions (Dapr). Learn Spec-Driven Development (SDD) fundamentals: write specifications, AI generates infrastructure, you validate. Establish professional thinking patterns for production deployment, not manual configuration.
Module 2Docker Fundamentals: Containerizing AI Applications
Containerize FastAPI services using Docker with AIDD and SDD. Specify requirements—multi-stage builds, Python dependencies, layer optimization—and Claude Code generates production Dockerfiles. Master container networking, health checks, and Docker Compose for local development. Focus on specification and validation, not Dockerfile syntax memorization.
Module 3Kubernetes Basics: Orchestrating Agent Systems
Orchestrate agent systems on Kubernetes with AIDD and SDD using kubectl-ai and kagent. Specify deployment requirements—pods, services, ConfigMaps, StatefulSets—and Claude Code generates manifests. Master Kubernetes primitives through specifications, event-driven patterns with Kafka, and production-grade configurations while AI handles YAML complexity.
Module 4DAPR Core: Cloud-Agnostic Abstractions
Implement Dapr Core and Dapr Workflows for cloud-agnostic communication and long-running processes. Specify requirements—state stores, pub/sub, service invocation, durable workflows—and Claude Code generates Dapr components. Master cloud-portable patterns: state works with any database, pub/sub with any broker, workflows for multi-step agent orchestration. Write once, deploy anywhere.
Module 5Production Operations: Observability, Scaling & CI/CD
Build production-ready AI systems using SDD for operations and monitoring. Specify observability requirements—OpenTelemetry traces, metrics, cost dashboards—and Claude Code generates telemetry configurations. Master autoscaling, CI/CD with Testcontainers and GitHub Actions, Infrastructure-as-Code with Terraform. Design through specifications, validate with confidence, operate at scale.
Course Outcomes
Apply Context Engineering to structure effective AI collaboration for infrastructure design
Partner with Claude Code to generate production-ready cloud configurations from specifications
Master Spec-Driven Development (SDD) to design infrastructure through clear intent, not manual YAML
Containerize AI applications with Docker using AIDD and SDD for multi-stage builds and optimization
Orchestrate agent systems on Kubernetes with AIDD and SDD using kubectl-ai and kagent
Implement Dapr Core and Dapr Workflows for cloud-agnostic state, pub/sub, and long-running processes
Build observable, scalable AI systems with OpenTelemetry, autoscaling, and automated CI/CD pipelines
Prerequisites
Note: These prerequisites provide essential knowledge for success in this course. If you haven't completed these courses, consider taking them first or reviewing the relevant materials.