Master's thesis: Adaptation of MLOps for the integration of AI/ML in maintenance

Master's thesis: Adaptation of MLOps for the integration of AI/ML in maintenance

• Part-time

The Service Management department optimizes processes in the operation, maintenance and repair of technical systems. We use modern technologies and innovative concepts to create sustainable added value. MLOps play a decisive role in efficiently implementing data-driven approaches in the context of AI and ML.

Thesis objective: The aim of your thesis is to adapt MLOps approaches specifically to the use case of maintenance. You will develop an approach that enables the effective use of AI/ML in maintenance processes and is supported by MLOps. Based on your results, you will derive recommendations for the implementation of customised MLOps pipelines.

Your tasks:

• Literature and market research on current trends and technologies in the field of MLOps and their application in maintenance
• Analysis of specific maintenance requirements for AI/ML models and their operation
• Development of an adapted MLOps approach that facilitates the integration of AI/ML in maintenance
• Derivation of recommendations for action and creation of practice-oriented documentation

Your profile:

• You are studying industrial engineering, mechanical engineering, computer science or similar
• You have very good written and spoken English skills
• You are characterised by an independent and committed as well as careful and goal-oriented way of working
• You are confident in using the common MS Office programmes

Our offer:

• Insights into the industrial and research business in collaboration with well-known companies and research partners
• Interesting, challenging and varied tasks in a qualified and dynamic team
• The opportunity for flexible time management and independent work
• A modern, collegial and digital working environment
• Room for creativity and your personal development