Azure AI for Cloud Engineers: Cloud Resources, Security, and Operations

Course overview

The training "Azure AI for Cloud Engineers: Cloud Resources, Security, and Operations" provides fundamental and advanced knowledge in the areas of "Cloud Resources", "Security", and "Operations". The focus is on the competence field of Azure AI and the application focus of Cloud Computing. The structure follows typical work steps from requirement gathering through technical implementation to testing and documentation of results. The exercises address Azure AI in a clear workflow and incorporate Cloud Computing at appropriate decision points. The guiding theme is the task "Azure AI for Cloud Engineers"; it is addressed from a technical, methodological, and quality-related perspective. In the results control, both Azure AI and Cloud Computing are examined based on understandable criteria. Participants work on comprehensible practical cases from cloud-based IT environments, compare solution paths, and document assumptions, decisions, and results. The goal is a structured understanding of the tasks described in the title and their application in different work situations. Program Participants compare approaches in which the focuses of Azure AI and Cloud Computing cover different requirements. The educational content is divided into seven interrelated learning sections: - Cloud Context and Responsibilities: The focus "Azure AI for Cloud Engineers" is contextualized regarding task framework, typical areas of application, and the necessary work steps. The orientation is specified through implementation steps, tool selection, integration, and the examination of technically robust results, as well as through the selection of suitable resources, provisioning, access, monitoring, and operational impacts. - Cloud Resources: This subtopic is addressed based on key terms, typical work steps, and a comprehensible application case. Knowledge of Azure AI is built up for the practical cases and linked to technical references to Cloud Computing. - Security: Permissions and rules are examined in such a way that responsibilities, evidence, and impacts of changes remain recognizable. - Operations: The focus is on reliable processes, controlled changes, and documented error handling. Finally, work results from Azure AI are reflected upon and compared with the learning objectives for Cloud Computing. - Technical Work Tasks and Tool Selection: A specific task is broken down into work steps, implemented with suitable tools, and verified against clear acceptance criteria. - Monitoring and Traceability: Reports, key figures, and configuration changes are integrated into a structured control process. The transfer part deepens Azure AI and translates the insights into a coherent task related to Cloud Computing. - Application Task and Reflection: A realistic example is planned, implemented, and evaluated based on documented criteria. The learning tasks connect Azure AI with Cloud Computing and make the interfaces between both topics visible. Based on professional situations,…

Upcoming dates

• 2026-08-09T22:00:00.000Z · 2026-11-01T23:00:00.000Z · München · Combined Learning
• 2026-08-09T22:00:00.000Z · 2027-01-24T23:00:00.000Z · München · Combined Learning
• 2026-09-06T22:00:00.000Z · 2026-11-29T23:00:00.000Z · München · Combined Learning
• 2026-09-06T22:00:00.000Z · 2027-02-21T23:00:00.000Z · München · Combined Learning