MLOps Community
The MLOps Community is where machine learning practitioners come together to define and implement MLOps.
Our global community is the default hub for MLOps practitioners to meet other MLOps industry professionals, share their real-world experience and challenges, learn skills and best practices, and collaborate on projects and employment opportunities. We are the world's largest community dedicated to addressing the unique technical and operational challenges of production machine learning systems.

Events
5:00 PM - 6:30 PM, Jun 10 GMT
GenAi in Games, 3D Animation and VFX
5:00 PM - 9:00 PM, May 28 PDT
AI Agent Builders Summit: World Tour Kickoff
4:00 PM - 5:00 PM, Apr 23 GMT
Iceberg, MCP, and MLOps: Bridging the gaps for Enterprise
Content
video
Willem Pienaar and Shreya Shankar discuss the challenge of evaluating agents in production where "ground truth" is ambiguous and subjective user feedback isn't enough to improve performance.
The discussion breaks down the three "gulfs" of human-AI interaction—Specification, Generalization, and Comprehension—and their impact on agent success.
Willem and Shreya cover the necessity of moving the human "out of the loop" for feedback, creating faster learning cycles through implicit signals rather than direct, manual review.
The conversation details practical evaluation techniques, including analyzing task failures with heat maps and the trade-offs of using simulated environments for testing.
Willem and Shreya address the reality of a "performance ceiling" for AI and the importance of categorizing problems your agent can, can learn to, or will likely never be able to solve.
Jun 13th, 2025 | Views 27
video
AI is already complex—adding the need for deep engineering expertise to use MLOps tools only makes it harder, especially for SMEs and research teams with limited resources. Yet, good MLOps is essential for managing experiments, sharing GPU compute, tracking models, and meeting AI regulations.
While cloud providers offer MLOps tools, many organizations need flexible, open-source setups that work anywhere—from laptops to supercomputers. Shared setups can boost collaboration, productivity, and compute efficiency.
In this session, Jukka introduces an open-source MLOps platform from Silo AI, now packaged for easy deployment across environments. With Git-based workflows and CI/CD automation, users can focus on building models while the platform handles the MLOps.
Jun 10th, 2025 | Views 50
video
Tecton Founder and CEO Mike Del Balso talks about what ML/AI use cases are core components generating Millions in revenue. Demetrios and Mike go through the maturity curve that predictive Machine Learning use cases have gone through over the past 5 years, and why a feature store is a primary component of an ML stack.
Jun 6th, 2025 | Views 65