Do you have what it takes to run AI in production?
Summary
A podcast discussion from HumanX on May 26, 2026, features Ryan Donovan and Peter Salanki, CTO and co-founder of CoreWeave, addressing the practicalities of running AI in production. The conversation highlights the critical importance of robust observability, efficient resource utilization, and effective scheduling for AI workloads. Salanki also advises against the common pitfall of over-architecting solutions too early in the development cycle. CoreWeave, described as an AI-native platform cloud, is purpose-built to provide the next-generation infrastructure and intelligent tools necessary to power the world's most complex AI workloads, underscoring its relevance to the discussion on production AI challenges.
Key takeaway
For AI Architects and MLOps Engineers deploying AI in production, prioritize robust observability, efficient resource utilization, and intelligent scheduling from the outset. Your focus should be on practical operational needs rather than premature, complex architectural designs. Consider platforms like CoreWeave that offer purpose-built infrastructure and tools to manage complex AI workloads, ensuring your production environment is scalable and maintainable.
Key insights
Successfully running AI in production demands focus on observability, utilization, scheduling, and avoiding premature over-architecture.
Principles
- Prioritize observability, utilization, and scheduling.
- Avoid over-architecting AI solutions too early.
In practice
- Evaluate AI infrastructure for observability features.
- Optimize resource utilization for complex AI workloads.
- Implement effective scheduling for production AI.
Topics
- AI in Production
- MLOps
- Observability
- Resource Utilization
- AI Infrastructure
- CoreWeave
Best for: MLOps Engineer, AI Architect, Director of AI/ML
Related on AIssential
Editorial summary, takeaway, and curation by AIssential. Original article published by Stack Overflow Blog.