MLOps in Practice: From Isolated Prototypes to Stable AI Operations
On-Demand Webinar
Data & Cloud Services
Controlling Costs in Engineering Development with AI
Many companies have developed effective machine learning prototypes —yet still fail when deploying them in production. The reason for this rarely lies in the quality of the models, but rather in organizational factors such as a lack of structure, unclear responsibilities, and insufficient automation.
In the on-demand webinar " ," our experts demonstrate how you can effectively resolve these hidden bottlenecks using Machine Learning Operations (MLOps). Specifically, you’ll learn how robust processes, governance, and automation pave the way from isolated prototypes to reliable, cost-effective AI operations.
The webinar will focus on:
- Why MLOps? How MLOps bridges the gap between data science and robust IT processes to reliably, scalably, and efficiently deploy AI models into production.
- Maturity Level & GAP Analysis: Assessment of existing infrastructure, processes, and roles, as well as identification of technical and organizational gaps compared to best practices.
- Sample Roadmap: An overview of relevant tools, CI/CD, and monitoring approaches, as well as the development of a sample roadmap featuring 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. Sound interesting? Then watch the on-demand webinar now:
Webinar Participants
Julian Schütt has been advising our clients for over 15 years on everything from the design to the implementation of smart data architectures. As head of the Data & Cloud Services business unit, he focuses on the use of innovative technologies, ranging from agile cloud environments to the efficient use of artificial intelligence.
Dr. Philip Gouverneur is a Senior Data Scientist at celver AG. Drawing on his background—a Ph.D. 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, among others, cost forecasting, time series forecasting, generative AI, and explainable AI.
