Foundations of Cloud AI Engineering

Course Overview

This foundational course introduces the AI engineering lifecycle and the architectural patterns behind cloud-based AI systems, distinguishing the discipline from data science and machine learning by focusing on how AI is actually built, deployed, and operated at scale. You’ll work through the four pillars of the space: the AI engineering process itself, cloud-based system architectures (including microservices, serverless, and hybrid deployment options), the different ways AI services can be delivered (AIaaS, PaaS, IaaS, managed versus self-hosted), and the sovereignty and compliance considerations unique to European cloud environments.

As the opening course in the AI Engineer learning path, it exists to answer a practical question before you touch any code: what are the real trade-offs between convenience and control when you put an AI system into production on a sovereign cloud like STACKIT? Every later course in the path — from LLM deployment to agent orchestration — assumes you already understand these delivery models and the GDPR and EU AI Act constraints that shape them, so this is where that shared vocabulary gets built.

What You’ll Learn

• Explain the AI engineering lifecycle and how it differs from a pure data-science workflow
• Compare cloud AI architecture patterns, including microservices, serverless, and hybrid deployments
• Evaluate AIaaS, PaaS, and IaaS delivery models to choose the right level of managed versus self-hosted control
• Identify data sovereignty and compliance requirements that apply to AI systems running in Europe

Modules

• Introduction to AI Engineering
• Cloud AI Architecture Patterns
• AI Service Delivery Models
• Sovereignty and Compliance in AI Systems

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