MLOps in practice: From isolated prototypes to stable AI operations
Controlling costs in engineering development with AI
Many companies have developed good machine learning prototypes —and still fail in productive operation. The reason for this rarely lies in the quality of the models, but rather in organizational factors such as a lack of structures, unclear responsibilities, and a lack of automation.
In the webinar on March 3, 2026, at 10 a.m. , our experts will show you how to specifically resolve these hidden bottlenecks with machine learning operations (MLOps). Specifically, you will learn how stable processes, governance, and automation pave the way from isolated prototypes to reliable, economical AI operations.
The focus of the webinar:
- Why MLOps? How MLOps bridges the gap between data science and stable IT processes to bring AI models into operation reliably, scalably, and efficiently.
- Maturity level & GAP analysis: Evaluation of existing infrastructure, processes, and roles, as well as identification of technical and organizational gaps in comparison to best practices.
- Exemplary roadmap: Presentation of relevant tools, CI/CD, and monitoring approaches, as well as derivation of an exemplary roadmap with quick wins for future-proof ML operations.
Take this opportunity to learn how MLOPs can help you create a stable, efficient, and future-proof environment for your AI initiatives.
Sounds exciting? Then register now for the free webinar:
Speaker
Julian Schütt has been advising our customers for over 15 years, from the conception to the implementation of smart data architectures. As head of the Data & Cloud Services business unit, he is involved in the use of innovative technologies, from agile cloud environments to the efficient use of artificial intelligence.
Dr. Philip Gouverneur is a Senior Data Scientist at celver AG. With his background knowledge from a doctorate in computer science, with a focus on machine learning and deep learning in time series analysis, he focuses on all topics related to artificial intelligence. These include cost forecasting, time series forecasting, genAI and explainable AI.
