AI Management: Quality Assurance of AI Systems

Course overview

An AI system can produce plausible-sounding but incorrect results or disadvantage certain groups. Quality assurance in AI management defines how results are checked, risks are assessed, and rules are followed. What you will learn Define quality criteria for AI systems Translate risks and regulations of AI into testing processes Apply AI ethics and governance frameworks Establish KPIs for AI success and quality Ensure data quality as the foundation of AI quality Prepare quality reports using Power BI Structure At the beginning, you will cover the fundamentals of AI strategy and model selection. This is followed by data for AI, governance structures, GDPR, and AI regulations. In the main part, you will address risks, AI ethics, KPIs, and collaboration with external partners whose solutions need to be tested. Case studies will demonstrate how testing rules are applied before deployment and during ongoing operations. Excel and Power BI will provide evaluations, and data storytelling will assist in communicating testing results. Perspectives As an AI Quality Manager, Responsible AI Officer, or Governance Analyst, you will ensure the quality of AI systems. In AI management, quality assurance becomes an integral part of every implementation, from selection to ongoing operations.

Upcoming dates

• 2026-11-22T23:00:00.000Z · 2027-01-30T23:00:00.000Z · Berlin · Combined Learning
• 2026-11-22T23:00:00.000Z · 2027-01-30T23:00:00.000Z · Berlin · Combined Learning
• 2026-11-22T23:00:00.000Z · 2027-01-30T23:00:00.000Z · München · Combined Learning
• 2026-11-22T23:00:00.000Z · 2027-01-30T23:00:00.000Z · Stuttgart · Combined Learning
• 2026-11-22T23:00:00.000Z · 2027-01-30T23:00:00.000Z · Frankfurt am Main · Combined Learning

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