Alan Shimel interviews Demetrios Brinkmann, founder of the MLOps Community, about the challenges and solutions for bringing AI from research into production, focusing on community-driven best practices and the operational complexities of modern AI systems.

MLOps at Scale: How Community Is Driving AI Into Production

Interview by Alan Shimel with Demetrios Brinkmann, founder of the MLOps Community

Overview

At swampUP 2025, Alan Shimel spoke with Demetrios Brinkmann about the pressing issue of bridging the gap between AI research and real-world production deployments. Brinkmann leads the MLOps Community, a global network of over 100,000 developers and practitioners focused on operationalizing AI and ML systems.

Community Initiatives

The MLOps Community offers a variety of resources for members:

  • Slack workspace for real-time collaboration
  • Curated one-on-one connections
  • In-person and virtual events, workshops, and conferences
  • A regular newsletter
  • Popular podcast series

These initiatives are aimed at increasing education, knowledge-sharing, and collaboration among practitioners facing similar challenges in deploying ML workloads.

Key Topics Discussed

1. Bridging the AI Research–Production Gap

While breakthroughs in AI research are frequent, bringing those ideas to production environments is complex and requires a different set of skills and processes. The MLOps Community helps practitioners learn from each other’s real-world deployment experiences instead of just theoretical demos.

2. Scaling Operational AI

Operationalizing AI at scale involves:

  • Reliable model deployment pipelines
  • Monitoring and observability for ML models
  • Managing technical debt from rapid experimentation
  • Governance and compliance challenges
  • Implementing CI/CD for machine learning models

The community highlights best practices and tools that enable robust operations for modern AI systems.

3. Responding to Generative and Agentic AI

The emergence of generative and agentic AI models has heightened the stakes. Teams now face increased pressure to move quickly, making it critical to implement appropriate safety, security, and compliance measures. Community forums provide a place to openly discuss the risks and solutions associated with these technologies.

4. Lessons and Best Practices

Practitioners in the MLOps Community collectively surface lessons learned, covering:

  • Model monitoring and drift detection
  • Managing the lifecycle of ML assets
  • Collaboration between data scientists, developers, and operations teams
  • Avoiding common pitfalls and overcoming organizational barriers

The Value of Community

Brinkmann emphasizes that AI cannot transform industries unless its deployment and operations are addressed systematically. Community-driven collaboration is key to accelerating successful adoption and reducing redundant trial and error.

Further Resources:

Author: Alan Shimel

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