CNCF-hosted Co-located Events Europe 2025

Cloud native AI/MLOps span a vast ecosystem of tools, architectures, and patterns that can be overwhelming for data scientists and developers alike. Many engineers are asked to implement AI/ML without an understanding of how fundamentally different models operate. Likewise, data scientists struggle to operationalize their work, lacking a background in engineering and DevOps practices.
This session will be a field guide to AI/MLOps tools and systems using the ML lifecycle as our map. At each stage of the ML lifecycle, we’ll identify the open source tools, DevOps practices, and cloud native infrastructure that support best practices in both data science and engineering.
Developers will gain an understanding of how to implement performant and efficient end-to-end AI/ML. Data scientists will gain an appreciation of how MLOps can enable rapid experimentation, model drift detection, and model integrity. All attendees will leave with take-home labs to begin their AI/ML deployment journeys.