Machine Learning with Azure AI: Training Data and AI Services

Course overview

The training course "Machine Learning with Azure AI: Training Data and AI Services" combines theoretical foundations with application-oriented exercises on the topics of "Training Data" and "AI Services". It examines how Azure AI is classified and the role of Machine Learning in professional situations. The structure leads from theoretical foundations through typical work steps to documented results and transfer tasks. The exercises deepen the understanding of Azure AI through a comprehensible practical case. The guiding theme is the task "Machine Learning with Azure AI"; it is addressed from a technical, methodological, and quality-related perspective. Documented results show how Azure AI and Machine Learning interact in the respective task area. 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 a structured understanding of the task areas described in the title and their application in different work situations. Program For Azure AI, selection criteria, work steps, and result characteristics are documented. The educational content is divided into seven interrelated learning sections: - Application Scenarios and Framework Conditions: The focus on "Machine Learning 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, provision, access, monitoring, and operational impacts. The transfer part deepens Azure AI and translates the insights into Machine Learning into a coherent task. - Training Data: Application cases are classified in terms of benefits, data reference, result quality, and responsible use. A dedicated learning step integrates Azure AI into the professional task framework. - AI Services: Use cases are described based on clear tasks; inputs, outputs, limitations, and control steps are examined together. - Result Verification: Quality criteria and test cases are established; deviations are assessed, and suitable improvements are derived. In the result control, both Azure AI and Machine Learning are examined based on comprehensible criteria. - Approach, Decision, and Documentation: Suitable work steps are selected, justified, and documented in a structured manner. - Result Quality, Limitations, and Responsibility: Outputs are checked for plausibility, traceability, and possible errors; limitations of use are documented. Knowledge of Azure AI is built for the practical cases and linked to technical references to Machine Learning. - Documented Final Example: The acquired knowledge is consolidated in a complete task and documented with work steps, decisions, and results. The exercises address Azure AI in a clear workflow and incorporate Machine Learning at appropriate decision points. The exercises are designed so that participants not only reproduce terms but also justi…

Upcoming dates

• 2026-08-23T22:00:00.000Z · 2026-11-15T23:00:00.000Z · München · Combined Learning
• 2026-08-23T22:00:00.000Z · 2027-02-07T23:00:00.000Z · München · Combined Learning
• 2026-09-20T22:00:00.000Z · 2026-12-13T23:00:00.000Z · München · Combined Learning
• 2026-09-20T22:00:00.000Z · 2027-03-07T23:00:00.000Z · München · Combined Learning