Cloud Machine Learning Engineer with Additional Qualification in Kanban

Course overview

The training course for Cloud Machine Learning Engineer is aimed at individuals who work with AI and Machine Learning (ML) in various professional and technical contexts. In many organizations, AI and Machine Learning (ML) are integral parts of daily practice, which is why the course repeatedly addresses AI and Machine Learning (ML). Introductory examples demonstrate how AI and Machine Learning (ML) are embedded in typical areas of responsibility and how requirements related to AI and Machine Learning (ML) can be systematically structured. As the content progresses, it explains how AI and Machine Learning (ML) are considered in the planning, implementation, and documentation of specific work steps. Participants will see how AI is used in various scenarios, while Machine Learning (ML) serves as a reference point for evaluations or configurations. Several practical sections explicitly address AI and Machine Learning (ML) to clarify how decisions regarding AI and Machine Learning (ML) impact the overall process. Another focus is on systematically addressing typical questions where AI and Machine Learning (ML) must be considered together. It becomes evident how AI serves as a foundation for certain work steps and how Machine Learning (ML) is used complementarily to structure or verify results. Participants will have the opportunity to apply AI and Machine Learning (ML) in different configurations and reflect on the role AI and Machine Learning (ML) play in their respective areas of responsibility. In the next step, it will be brought together how AI and Machine Learning (ML) are integrated into long-term developments. It will be shown how insights gained from AI and information processed with Machine Learning (ML) can be used for further decisions. Finally, there will be a reflection on how AI and Machine Learning (ML) will play a role in future projects and how a conscious approach to AI and Machine Learning (ML) contributes to making processes understandable and transparent. Agenda: 1. Classification of roles and areas of responsibility 2. Overview of typical work processes 3. Structuring of professional and technical requirements 4. Approaches to analysis, planning, and implementation 5. Use of supporting tools and platforms 6. Documentation and traceability of workflows 7. Collaboration with adjacent roles and areas 8. Long-term development of structures and processes At the end of the training, the following module will be the focus: "Kanban" imparts fundamental knowledge about the Kanban framework for visualizing and managing work processes. Participants will learn how to analyze workflows, identify bottlenecks, and increase team efficiency through continuous improvement.

Upcoming dates

• 2026-07-26T22:00:00.000Z · 2027-01-23T23:00:00.000Z · Eschborn, Taunus · Combined Learning
• 2026-07-26T22:00:00.000Z · 2026-10-24T22:00:00.000Z · Eschborn, Taunus · Combined Learning
• 2026-07-26T22:00:00.000Z · 2027-01-23T23:00:00.000Z · München · Combined Learning
• 2026-07-26T22:00:00.000Z · 2026-10-24T22:00:00.000Z · München · Combined Learning
• 2026-09-06T22:00:00.000Z · 2027-03-09T23:00:00.000Z · Eschborn, Taunus · Combined Learning