At swampUP 2025, Alan caught up with Demetrios Brinkmann, founder of the MLOps Community, to discuss the growing gap between AI research and real-world production deployments. Brinkmann leads a global network of more than 100,000 developers dedicated to bridging that divide, helping teams move beyond flashy demos and academic models to systems that deliver tangible business value.

The MLOps Community provides multiple ways for practitioners to engage: a Slack workspace, curated one-on-one matches, in-person meetups, workshops, conferences, virtual events, a newsletter, and a popular podcast. The goal, Brinkmann explained, is to raise the overall level of education and awareness across the field while connecting practitioners who are tackling similar challenges in bringing AI and ML into production.

One of the recurring themes is the operational complexity of scaling AI workloads. Many organizations can train models, but deploying them reliably, monitoring performance, and ensuring governance at scale requires different skill sets and tools. The community’s role is to share hard-won lessons from practitioners and highlight emerging best practices in areas like observability, CI/CD for ML models, and managing technical debt created by fast-moving experimentation.

Brinkmann noted that with the rise of generative and agentic AI, the stakes are even higher. Teams are under pressure to move fast, but deploying AI without proper guardrails risks security lapses, compliance failures, and business disruptions. The MLOps Community’s mission is to give practitioners a space to navigate these issues openly and collaboratively, rather than reinventing the wheel in isolation.

As Brinkmann put it, AI won’t transform industries unless it can be reliably operationalized — and that’s where communities like his make the difference.