MLOps Community
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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

6:55 PM - 8:00 PM, Feb 19 GMT
Agent Hour
AI Agents: Talks and Virtual RoundtablesWe're on a roll in 2025! Join us for our sixth bi-weekly Agent Hour as we continue the conversation about AI agents. Agent Hour Part #6 kicks off with 2 talks from Hassan Sawaf and Shrivu Shankar wrapping up with an
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1:00 PM - 6:00 PM, Feb 26 GMT
Nebius AI Cloud Unveiled. London Meetup

Content

video
There seems to be a shift from workflow languages to code - mostly annotation pythons - happening and getting us. It is a symptom of how complex workflow orchestration has gotten. Is it a dominant trend or will we cycle back to “DAG specifications”? At Stitchfix, we had our own DSL that “compiled” into airflow DAGs and at MicroByre, we used a external workflow langauge. Both had a batch task executor on K8s but at MicroByre, we had human and robot in the loop workflows.
Feb 14th, 2025 | Views 5
Blog
This survey examines popular workflow orchestration systems, highlighting their strengths, weaknesses, and key features. It provides insights into how these tools handle automation, scalability, and reliability, helping teams choose the right solution for their needs.
Feb 13th, 2025 | Views 14
video
In this MLOps Community Podcast episode, Willem Pienaar, CTO of Cleric, breaks down how they built an autonomous AI SRE that helps engineering teams diagnose production issues. We explore how Cleric builds knowledge graphs for system understanding, and uses existing tools/systems during investigations. We also get into some gnarly challenges around memory, tool integration, and evaluation frameworks, and some lessons learned from deploying to engineering teams.
Feb 11th, 2025 | Views 43