BI Analysis with Azure AI: Data Models and AI Services

Course overview

In the training course "BI Analysis with Azure AI: Data Models and AI Services," the topics of "Data Models" and "AI Services" are systematically explored and deepened through professional tasks. Participants compare approaches in which the focuses of Azure AI and Business Intelligence cover different requirements. The focus is on recognizing relevant patterns, verifying statements, and presenting results in an understandable manner. The transfer part deepens Azure AI and translates the insights into Business Intelligence into a coherent task. The guiding theme is the task "BI Analysis with Azure AI"; it is addressed from a technical, methodological, and quality-related perspective. The technical focus is on the competence area of Azure AI and the application focus of Business Intelligence. Participants work on comprehensible practical cases from data-related analysis and platform tasks, compare solution paths, and document assumptions, decisions, and results. The goal is a structured understanding of the task areas described in the title and their application in different work situations. Program The technical classification of Azure AI is supplemented by concrete tasks related to Business Intelligence. The educational content is divided into seven interrelated learning sections: - Fundamentals of Evaluation and Provisioning: The focus "BI Analysis with Azure AI" is classified in terms of task framework, typical areas of application, and the necessary work steps. The orientation is specified through the selection of suitable resources, provisioning, access, monitoring, and operational impacts, as well as through technical questions, comprehensible evaluation paths, and a reasoned interpretation of results. Professional situations are examined to determine how Azure AI is classified and what role Business Intelligence plays in this context. - Data Models: Technical terms are transformed into consistent structures; keys, relationships, and usage scenarios are examined. Documented results show how Azure AI and Business Intelligence interact in their respective task areas. - AI Services: Application possibilities are described based on clear tasks; inputs, outputs, limitations, and control steps are jointly considered. - Preparation Steps: Participants classify requirements, choose an appropriate approach, and document the resulting outcomes. The learning tasks connect Azure AI with Business Intelligence and make the interfaces between the two topics visible. - Key Figures, Patterns, and Significance: Results are checked for plausibility, comparability, and technical relevance and are classified appropriately for the target audience. - Quality of Evaluation: Sources, assumptions, calculations, and statements are checked and documented clearly. Finally, work results from Azure AI are reflected upon and compared with the learning objectives for Business Intelligence. - Transfer to Professional Situations: Several typical situations are compared so th…

Upcoming dates

• 2026-08-30T22:00:00.000Z · 2026-11-22T23:00:00.000Z · München · Combined Learning
• 2026-08-30T22:00:00.000Z · 2027-02-14T23:00:00.000Z · München · Combined Learning
• 2026-09-27T22:00:00.000Z · 2026-12-20T23:00:00.000Z · München · Combined Learning
• 2026-09-27T22:00:00.000Z · 2027-03-14T23:00:00.000Z · München · Combined Learning