STACKIT Introduction
STACKIT
STACKIT AI Engineer
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STACKIT
STACKIT AI Engineer
Comprehensive learning path for AI engineers building sovereign AI solutions on STACKIT, from LLMs and agents to MLOps and responsible AI.
STACKIT Introduction
Guide
Advisory stackit university learning stackit overview sovereignty partner
STACKIT
The sovereign European cloud provider behind the framework, delivering IaaS and PaaS from German and Austrian data centers with full digital independence.
Course Overview
Section titled “Course Overview”An entry-level course that sets up everything else in STACKIT University. It explains STACKIT’s place inside the Schwarz Group and Schwarz Digits, walks through the three pillars behind the sovereign cloud promise — Scalable, Secure, Sovereign — and unpacks what each one actually guarantees in practice, from EU-only data centers to independence from foreign investors. From there it moves into the architecture itself: the IaaS and PaaS layers, how resources are managed through the Portal, API, CLI, and Terraform, and how the 6R framework (rehost, replatform, refactor, repurchase, retain, retire) maps existing workloads onto STACKIT services.
It’s built for anyone who needs a working mental model of STACKIT before going deeper — new customers, new partners, or new hires — so that later courses on the portfolio, portal, or sales don’t have to re-explain the basics. By the end, you’ll be able to place STACKIT correctly against hyperscalers and on-premise alternatives, and know which migration strategy fits a given workload.
What You’ll Learn
Section titled “What You’ll Learn”- Explain STACKIT’s relationship to the Schwarz Group and why that backing translates into financial and operational stability
- Describe what “Scalable, Secure, Sovereign” concretely guarantees — not just the slogan
- Distinguish STACKIT’s IaaS and PaaS layers and know which one a given use case calls for
- Match an existing workload to the right 6R migration strategy
- Navigate the STACKIT Portal, APIs, CLI, and Terraform provider as entry points for managing resources
Modules
Section titled “Modules”- STACKIT and the Schwarz Group
- Mission, Vision, and Core Values
- Key Benefits and Strategic Value
- Big Picture: STACKIT Architecture and Services
STACKIT Portal
Guide
STACKIT Portal
STACKIT
Advisory stackit university learning portal iam onboarding
STACKIT
The sovereign European cloud provider behind the framework, delivering IaaS and PaaS from German and Austrian data centers with full digital independence.
Course Overview
Section titled “Course Overview”A short, video-centric course that gets you comfortable in the STACKIT Portal: how projects, resources, and IAM tie together, how the interface is laid out, and how to assign users to a project. From there it moves into using the Portal day to day — reading full cost transparency into a project, choosing infrastructure services, and setting up platform services and runtimes — so you leave able to actually operate in the Portal, not just recognize it.
It’s designed as a quick second step right after the STACKIT Introduction course, before diving into the full service portfolio or a specific role path.
What You’ll Learn
Section titled “What You’ll Learn”- Explain STACKIT’s IAM and resource concept and how projects fit together
- Navigate the Portal interface and assign users to a project
- Read cost transparency data and use the cost calculator
- Set up IaaS servers and select infrastructure services in the Portal
- Launch platform services, runtimes, and PaaS components
- Know where to reach STACKIT Support from the Portal
Modules
Section titled “Modules”- Portal Fundamentals
- Portal Services
STACKIT Portfolio
Guide
STACKIT Portfolio
STACKIT
Advisory stackit university learning portfolio overview
STACKIT
The sovereign European cloud provider behind the framework, delivering IaaS and PaaS from German and Austrian data centers with full digital independence.
Course Overview
Section titled “Course Overview”A guided tour of STACKIT’s portfolio at a glance: what Availability Zones are and how they underpin reliability, how to tell IaaS, PaaS, and SaaS apart and pick the right one, and how cloud-native applications actually run on STACKIT’s runtime offerings. From there it covers modern data and AI services, security and compliance, and the supporting services and interfaces (Portal and APIs) that tie the whole portfolio together.
It’s the third step of the STACKIT Fundamentals path — after the Introduction and the Portal — and gives every other STACKIT University path a shared map of “what exists” before going deep on any one part of it.
What You’ll Learn
Section titled “What You’ll Learn”- Explain what STACKIT is and describe its core portfolio areas
- Understand Availability Zones and how STACKIT ensures reliability and high availability
- Distinguish IaaS, PaaS, and SaaS and know when to use each
- Describe how cloud-native applications run on STACKIT runtime offerings
- Place data, AI, cybersecurity, and critical infrastructure protection within the STACKIT ecosystem
- Get a high-level view of the STACKIT Portal and APIs as interfaces to manage services
Modules
Section titled “Modules”- STACKIT’s Portfolio at a Glance
- Cloud Infrastructure Fundamentals
- STACKIT Services for Application Deployment
- Platform & Developer Experience
- Modern Data & AI Services
- Security & Compliance
- STACKIT Supporting Services
Foundations of Cloud AI Engineering
Guide
Foundations of Cloud AI Engineering
STACKIT
Data and AI stackit university learning ai cloud ai architecture compliance
STACKIT
The sovereign European cloud provider behind the framework, delivering IaaS and PaaS from German and Austrian data centers with full digital independence.
Course Overview
Section titled “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
Section titled “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
Section titled “Modules”- Introduction to AI Engineering
- Cloud AI Architecture Patterns
- AI Service Delivery Models
- Sovereignty and Compliance in AI Systems
LLM Fundamentals
Guide
LLM Fundamentals
STACKIT
Data and AI stackit university learning ai llm rag
STACKIT
The sovereign European cloud provider behind the framework, delivering IaaS and PaaS from German and Austrian data centers with full digital independence.
Course Overview
Section titled “Course Overview”This course covers the theoretical and practical foundations of large language models, tracing the path from RNNs to the Transformer architecture and unpacking core concepts like tokenization, embeddings, and context windows before moving into how models are actually trained and adapted: pre-training on web-scale data, fine-tuning, instruction tuning, and RLHF. From there it shifts into application-building, teaching prompting techniques from zero-shot to chain-of-thought, and walking through how to build a simple retrieval-augmented generation (RAG) application.
The throughline is that none of this stays abstract — a dedicated module has you making authenticated API calls against STACKIT AI Model Serving to run text generation and embeddings against open-source models, so you leave with a working Python application rather than just an understanding of transformer math. That matters because most LLM tutorials assume a US hyperscaler API key; this one is built around keeping your prompts, responses, and data inside European infrastructure from day one, and closes with a module on bias, hallucination, and privacy risks specific to production LLM applications.
What You’ll Learn
Section titled “What You’ll Learn”- Explain transformer architecture, tokenization, and embeddings well enough to reason about model behavior
- Distinguish pre-training, fine-tuning, and RLHF, and know when each is the right tool
- Write effective prompts using zero-shot, few-shot, and chain-of-thought techniques
- Build a simple RAG application backed by STACKIT AI Model Serving
- Recognize hallucination, bias, and data-privacy risks before they reach production
Modules
Section titled “Modules”- Foundations of Large Language Models
- Training, Fine-Tuning, and Evaluation
- Prompt Engineering and Application Patterns
- Deploying LLMs with STACKIT AI Model Serving
- Ethics and the Future of LLMs
Using the STACKIT AI Model Service
Guide
Using the STACKIT AI Model Service
STACKIT
Data and AI stackit university learning ai model service inference rag deployment
STACKIT
The sovereign European cloud provider behind the framework, delivering IaaS and PaaS from German and Austrian data centers with full digital independence.
Course Overview
Section titled “Course Overview”This course teaches the practical skills for deploying and integrating the STACKIT AI Model Service into your own applications. You’ll start with the service’s architecture and supported model formats, then move into deploying models to endpoints, configuring resources, and setting up versioning and rollback strategies with proper health checks — the operational details that separate a working demo from something you’d actually run in production.
The second half is about keeping that deployment healthy and accountable once it’s live: configuring authentication and access policies, issuing and scoping API keys, monitoring performance and usage metrics, and optimizing cost and resource utilization on STACKIT’s managed, sovereign infrastructure. It’s a focused, hands-on follow-up to LLM Fundamentals — less about the theory of the models themselves, more about the service operations you need once you’re responsible for keeping them running.
What You’ll Learn
Section titled “What You’ll Learn”- Navigate the STACKIT AI Model Service architecture and its supported model formats and tiers
- Deploy models to endpoints with versioning and rollback strategies in place
- Configure authentication, API keys, and access policies for production use
- Monitor performance, usage, and cost, and troubleshoot with logs when something breaks
Modules
Section titled “Modules”- Service Architecture and Capabilities
- Model Deployment and Configuration
- Access Management and Performance Monitoring
AI Agents Fundamentals
Guide
AI Agents Fundamentals
STACKIT
Data and AI stackit university learning ai agents mcp langchain multi-agent
STACKIT
The sovereign European cloud provider behind the framework, delivering IaaS and PaaS from German and Austrian data centers with full digital independence.
Course Overview
Section titled “Course Overview”This is a “zero to hero” course: it assumes no prior exposure to AI agents and builds from agent architecture (perception, reasoning, action) and reactive versus deliberative designs up through the frameworks used to actually build them — LangChain, AutoGen, CrewAI, and Semantic Kernel. Along the way it gives real weight to the emerging standardized protocols, Model Context Protocol (MCP) and Agent2Agent (A2A), that let agents call tools and talk to each other instead of being locked into one vendor’s walled garden.
The back half moves past single-agent demos into what production actually looks like: multi-agent orchestration and consensus, streaming responses over WebSockets and SSE for real-time interaction, and the alignment, security, and regulatory questions that come with letting an autonomous system take actions on your behalf. It closes with a capstone module where you design and document a complete agent solution end to end, rather than just reading about one — the same “own your build” philosophy carried through the rest of the AI Engineer path.
What You’ll Learn
Section titled “What You’ll Learn”- Design agent architectures and choose between reactive and deliberative approaches
- Build agents with frameworks like LangChain, AutoGen, or CrewAI, and connect them via MCP and A2A
- Coordinate multi-agent systems with orchestration and consensus patterns
- Implement streaming, real-time agent interactions over WebSockets and SSE
- Evaluate the security, alignment, and regulatory risks of running agents in production
Modules
Section titled “Modules”- AI Agent Foundations
- Agent Communication Protocols
- Agent Development Frameworks
- Industry Applications and Case Studies
- Multi-Agent Systems and Orchestration
- Streaming and Real-Time Interactions
- Ethics, Security, and Future Trends
- Capstone Project and Professional Development
MLOps Fundamentals on STACKIT
Guide
MLOps Fundamentals on STACKIT
STACKIT
Data and AI stackit university learning mlops ai
STACKIT
The sovereign European cloud provider behind the framework, delivering IaaS and PaaS from German and Austrian data centers with full digital independence.
Course Overview
Section titled “Course Overview”MLOps bridges the gap between model development and production deployment by applying DevOps principles to machine learning workflows. This course teaches how to build robust ML pipelines, manage model lifecycles, and keep AI applications reliable in production — all on STACKIT’s sovereign cloud infrastructure.
Across five modules it covers MLOps principles and the maturity model, CI/CD pipelines for ML models, experiment tracking and model versioning with MLflow, model registries and deployment strategies (blue-green, canary, A/B), and monitoring, drift detection, and observability for AI systems in production.
What You’ll Learn
Section titled “What You’ll Learn”- Understand MLOps principles and how they bridge development and production
- Design and implement CI/CD pipelines for machine learning models
- Set up experiment tracking and model versioning with MLflow on STACKIT
- Configure model registries and deployment strategies including blue-green, canary, and A/B patterns
- Implement monitoring, drift detection, and observability for AI applications in production
Modules
Section titled “Modules”- Introduction to MLOps and STACKIT Infrastructure
- CI/CD Pipelines for ML Models
- Experiment Tracking and Model Versioning
- Model Registry and Deployment Strategies
- Monitoring and Observability
Responsible AI and European Compliance
Policy
Responsible AI and European Compliance
STACKIT
Data and AI stackit university learning eu ai act gdpr responsible ai bias
STACKIT
The sovereign European cloud provider behind the framework, delivering IaaS and PaaS from German and Austrian data centers with full digital independence.
Course Overview
Section titled “Course Overview”This course covers the legal and ethical guardrails for deploying AI in Europe, starting with the EU AI Act’s risk-based classification system — what counts as a prohibited practice, what counts as high-risk, and how that interacts with GDPR — before moving into the specifics of Article 22 and automated decision-making, Data Protection Impact Assessments, and the legal bases you need to process data in an AI system at all.
From there it turns practical: implementing explainable AI (XAI) techniques, measuring and mitigating bias with real fairness metrics, and applying privacy-preserving methods like differential privacy and federated learning so that compliance is built into the system’s design rather than bolted on afterward. This isn’t abstract policy reading — it’s the compliance foundation the capstone course later expects you to defend in front of an actual compliance officer, and it’s essential for anyone deploying AI systems that touch European data or European markets.
What You’ll Learn
Section titled “What You’ll Learn”- Classify an AI system under the EU AI Act’s risk tiers and identify prohibited practices
- Apply GDPR Article 22 and conduct a Data Protection Impact Assessment for an AI system
- Measure bias with fairness metrics and implement explainable AI techniques
- Apply privacy-preserving methods such as differential privacy and federated learning
Modules
Section titled “Modules”- European AI Regulatory Landscape
- GDPR and AI Systems
- Transparency, Explainability, and Fairness
- Privacy-Preserving AI and Best Practices
Capstone: Your Sovereign AI Assistant with OpenClaw
Guide
Capstone: Your Sovereign AI Assistant with OpenClaw
STACKIT
Data and AI stackit university learning capstone openclaw ai agent compute engine
STACKIT
The sovereign European cloud provider behind the framework, delivering IaaS and PaaS from German and Austrian data centers with full digital independence.
Course Overview
Section titled “Course Overview”In this self-directed capstone, you deploy OpenClaw — a self-hosted, open-source personal AI assistant that went from zero to one of the most-starred repositories on GitHub in days, precisely because it puts control back in the operator’s hands instead of a corporate cloud you can’t inspect — on STACKIT Compute Engine, connect it to STACKIT AI Model Serving, and extend it with custom agents and integrations. There are no templates and no step-by-step walkthroughs here: you’ve completed the rest of the AI Engineer learning path, and this is where you prove you can combine those skills independently.
The result is a production-hardened, fully sovereign AI assistant where every prompt, response, and byte of memory stays within European data centers — something you can actually use day to day, and something you can defend in front of a compliance officer. Mastery is judged across four dimensions rather than a checklist: sovereignty rigor (can you trace every data flow and enforce boundaries at the infrastructure level, not just document them), a working deployment connected to real messaging integrations, sound architectural thinking captured in an Architecture Decision Record, and creative problem-solving in how far you push the custom agents and integrations beyond the minimum.
What You’ll Learn
Section titled “What You’ll Learn”- Deploy and operate OpenClaw as a self-hosted, sovereign personal AI assistant on STACKIT Compute Engine
- Wire STACKIT AI Model Serving in as the LLM backend so no prompt, response, or memory leaves Europe
- Trace and enforce data-sovereignty boundaries at the infrastructure level using Security Groups and secrets management
- Extend OpenClaw with custom agents, including a compliance-monitoring agent
- Harden a production deployment for observability, resilience, and GDPR-readiness, and justify the design in an ADR
Modules
Section titled “Modules”- The Architecture of a Personal AI OS
- OpenClaw Meets STACKIT AI Model Serving
- Deploy on STACKIT Compute Engine
- Custom Skills and Sovereign Integrations
- Production Hardening and the Final Challenge
Trail historyActive 4 of the last 12 weeksTMUpdatedNo updates · 1 bar = 1 week i
Maintainers
- TMTobias M.Head of STACKIT Cloud Framework · STACKITOwnerTobias M.Head of STACKIT Cloud Framework · STACKITOwnerActive 12 of the last 12 weeks · 168 updateswww.linkedin.com/in/tobias-müller-011304172
TMTobias M.Head of STACKIT Cloud Framework · STACKITOwnerActive 12 of the last 12 weeks · 168 updatesSTACKITwww.linkedin.com/in/tobias-müller-011304172CC.C1SCF Core · STACKITOwnerActive 5 of the last 12 weeks · 23 updatesSTACKITwww.linkedin.com/in/can-celik-645932315can.celik1@digits.schwarzContributed in STACKIT
Tobias M.Head of STACKIT Cloud Framework · STACKITOwnerActive 12 of the last 12 weeks · 168 updatesSTACKITwww.linkedin.com/in/tobias-müller-011304172CC.C1SCF Core · STACKITOwnerActive 5 of the last 12 weeks · 23 updatesSTACKITwww.linkedin.com/in/can-celik-645932315can.celik1@digits.schwarzContributed in STACKITTobias M.
Tobias M.Head of STACKIT Cloud Framework · STACKITOwnerActive 12 of the last 12 weeks · 168 updatesSTACKITwww.linkedin.com/in/tobias-müller-011304172
· Aug 26, 2026Tobias M.
Tobias M.Head of STACKIT Cloud Framework · STACKITOwnerActive 12 of the last 12 weeks · 168 updatesSTACKITwww.linkedin.com/in/tobias-müller-011304172
· Aug 19, 2026C.C1
C.C1SCF Core · STACKITOwnerActive 5 of the last 12 weeks · 23 updatesSTACKITwww.linkedin.com/in/can-celik-645932315can.celik1@digits.schwarz
· Aug 10, 2026
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C.C1
C.C1SCF Core · STACKITOwnerActive 5 of the last 12 weeks · 23 updatesSTACKITwww.linkedin.com/in/can-celik-645932315can.celik1@digits.schwarz
· Aug 10, 2026C.C1
C.C1SCF Core · STACKITOwnerActive 5 of the last 12 weeks · 23 updatesSTACKITwww.linkedin.com/in/can-celik-645932315can.celik1@digits.schwarz
· Aug 4, 2026