AI Platforms for Machine Learning: Service Selection and Training Data

Course overview

The training course "AI Platforms for Machine Learning: Service Selection and Training Data" gradually introduces the topics of "Service Selection" and "Training Data" and translates the content into understandable work situations. For Artificial Intelligence, selection criteria, work steps, and result characteristics are documented. The structure leads from the technical fundamentals through typical work steps to documented results and transfer tasks. The transfer part deepens Machine Learning and translates the insights into Artificial Intelligence into a coherent task. The guiding theme is the task "AI Platforms for Machine Learning"; it is addressed from a technical, methodological, and quality-related perspective. Commonalities, differences, and meaningful transitions between Machine Learning and Artificial Intelligence are elaborated. Participants work on understandable 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 At the end, work results from Machine Learning are reflected upon and compared with the learning objectives for Artificial Intelligence. The educational content is divided into seven interrelated learning sections: - Task Areas and AI Reference: The focus on "AI Platforms for Machine Learning" is categorized in terms of task framework, typical application areas, and the necessary work steps. The orientation is specified through interfaces, dependencies, and controlled transitions between several work or system areas. In the result control, both Machine Learning and Artificial Intelligence are considered based on understandable criteria. - Service Selection: Participants categorize requirements, choose an appropriate approach, and document the resulting outcomes. - Training Data: Application cases are categorized in terms of benefits, data reference, result quality, and responsible use. In Artificial Intelligence, the focus is on a proper and understandable approach. - Limits and Responsibility: This subtopic is addressed using central terms, typical work steps, and a comprehensible application case. - Approach, Decision, and Documentation: Suitable work steps are selected, justified, and documented in a structured manner. The technical focus is on the competence field of Machine Learning and the application focus of Artificial Intelligence. - Control and Responsible Use: Inputs, results, and human review steps are connected to a comprehensible quality process. - Application Task and Reflection: A realistic example is planned, implemented, and evaluated based on documented criteria. The learning tasks connect Machine Learning with Artificial Intelligence and make the interfaces between both topics visible. The application of Artificial Intelligence is reinforced through structured tasks and document…

Upcoming dates

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