A hands-on course teaching doctoral researchers to build production-grade AI systems with emphasis on reliability, safety, and real-world deployment—perfectly aligned with MDSI's relAI initiative.
Five critical alignment points that make this course essential for TUM's doctoral researchers
Every module emphasizes reliable, trustworthy AI—from safe deployment to security and governance. Your relAI initiative focuses on reliability research; this course teaches reliability engineering.
100% AlignmentMixed-domain team projects force cross-specialty collaboration. Researchers from biology, physics, CS, and social sciences build systems together—exactly MDSI's vision.
Core MissionUnlike pure theory courses, this prepares researchers for BOTH academia (research tools, reproducibility) AND industry (production frameworks, MLOps, deployment).
Dual PathFits as core MDSI technical module AND supports all three focus tracks (Research, Entrepreneurship, Communication). Complete deliverables meet all requirements.
Full IntegrationResearchers learn algorithms everywhere. What's missing: production engineering—deployment, monitoring, security, governance. This course fills that specific gap.
Unique ValueAll materials ready. Three flexible formats. No development work needed from MDSI. Instructor with proven production expertise. Ready to launch immediately.
Ready NowTheory-first, practice-heavy approach building production AI systems step by step
Ground researchers in LLM architectures, adaptation strategies (RAG vs fine-tuning), and prompt engineering patterns (CoT, ReAct, verification).
Master experiment tracking, deployment strategies (canary, shadow), monitoring, and drift detection for reliable ML systems.
Learn simulation-based validation, what-if scenarios, intent verification systems, and evidence-based decision making.
Architect AI agents with planning, tools, memory, and reflection. Implement safety constraints and human-in-the-loop workflows.
Design agent collaboration patterns, implement communication protocols (A2A, ACP, MCP), and orchestrate complex workflows.
Implement security controls, privacy-preserving techniques (DP, FL), and governance frameworks for trustworthy AI deployment.
Teams design and build complete AI systems addressing real research problems in their domains. Includes architecture, implementation, evaluation, safety controls, and presentation.
Every module emphasizes reliability, safety, and trustworthiness—the core mission of TUM's relAI initiative
Every AI output verified against ground truth before deployment—no blind trust in model outputs.
Tool allowlists, schema validation, human oversight, and rollback readiness built into every system.
Monitoring, drift detection, performance tracking, and alerting—know when systems deviate from expected behavior.
Threat models, prompt injection defense, data isolation, and privacy-preserving techniques integrated from day one.
Audit trails, evidence packs, approval workflows, and compliance frameworks for accountable AI.
Digital twins enable safe experimentation—test changes in sandbox before production deployment.
CTO & Founder, vExpertAI | Production AI Expert | Critical Infrastructure Specialist
As CTO of vExpertAI, Eduard specializes in building AI-powered solutions for critical infrastructure—combining deep expertise in information security, network architecture, and production AI systems. His work focuses on making AI reliable, secure, and trustworthy for real-world deployment.
Skills that advance both academic research and industry career paths
Let's discuss how Applied AI Engineering Lab can strengthen MDSI's doctoral training program and support the relAI initiative.