Data Platforms with Azure AI: Data Acquisition and AI Services

Course overview

In the continuing education program "Data Platforms with Azure AI: Data Acquisition and AI Services," the topics of "Data Acquisition" and "AI Services" are systematically explored and deepened through professional tasks. The focus of Azure AI is on a proper and comprehensible approach. Central to the program are a clear task reference, suitable methodologies, and verifiable work results. Professional situations are examined to determine how Azure AI is categorized and what role data platforms play in this context. The guiding theme is the task "Data Platforms with Azure AI," which is addressed from a technical, methodological, and quality-related perspective. Participants compare approaches where the focuses of Azure AI and data platforms cover different requirements. Participants work on comprehensible practical cases from AI-supported work and application situations, compare solution paths, and document assumptions, decisions, and results. The goal is to achieve a structured understanding of the task areas described in the title and their application in various work situations. Program The exercises address Azure AI in a clear workflow and incorporate data platforms at appropriate decision points. The educational content is divided into seven interrelated learning sections: - Task Areas and AI Reference: The focus "Data Platforms with Azure AI" is categorized concerning task frameworks, typical areas of application, and the necessary work steps. The orientation is specified through the selection of suitable resources, provision, access, monitoring, and operational impacts, as well as the path from sources and structures through processing steps to usable and verified results. - Data Acquisition: Data paths are planned from the source to the destination; transformations, dependencies, and error paths are documented. Typical requirements, possible solution paths, and evaluation criteria for Azure AI are compared. - AI Services: Application cases are categorized concerning benefits, data reference, result quality, and responsible use. - Task Formulation: The subtopic is addressed using central terms, typical work steps, and a comprehensible application case. In the result control, both Azure AI and data platforms are examined based on understandable criteria. - Approach, Decision, and Documentation: Suitable work steps are selected, justified, and documented in a structured manner. - Result Quality, Limits, and Responsibility: Outputs are checked for plausibility, comprehensibility, and possible errors; limits of use are documented. Documented results show how Azure AI and data platforms interact in the respective task area. - Application Task and Reflection: A realistic example is planned, implemented, and evaluated based on documented criteria. The learning tasks connect Azure AI with data platforms and make the interfaces between both topics visible. The transfer to new situations is explicitly practiced for Azure AI. The exercises are design…

Upcoming dates

• 2026-08-16T22:00:00.000Z · 2026-11-08T23:00:00.000Z · München · Combined Learning
• 2026-08-16T22:00:00.000Z · 2027-01-31T23:00:00.000Z · München · Combined Learning
• 2026-09-13T22:00:00.000Z · 2026-12-06T23:00:00.000Z · München · Combined Learning
• 2026-09-13T22:00:00.000Z · 2027-02-28T23:00:00.000Z · München · Combined Learning