MLOps in practice: From isolated prototypes to stable AI operations

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 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 focus of the webinar: 

• 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: Evaluation of existing infrastructure, processes, and roles, as well as identification of technical and organizational gaps in comparison 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: