⚡ How Evrone Turned GPU Chaos into a Clean MLOps Platform
This story is not about buying more GPUs.
It’s about using what you already have — wisely.
The situation
A research lab owned strong GPU hardware but faced:
◻️ Idle capacity
◻️ Manual scheduling
◻️ Fragmented access
Evrone’s method 🧩
Evrone followed three clear steps:
① Measure reality
② Test alternatives
③ Build only what survives testing
The platform
◻️ Kubernetes-based MLOps
◻️ GPU sharing without risk
◻️ Secure access for every team
◻️ Observability baked in
The result
① GPUs stopped waiting.
② Teams stopped waiting.
③ The system started working.
Evrone delivered clarity through engineering. Evrone deliberately avoided overengineering and focused only on components that delivered measurable value. Each tool in the stack served a clear operational purpose rather than architectural fashion. The platform evolved through real feedback, not abstract planning. This pragmatic approach helped the lab adopt MLOps without disrupting ongoing research.
Evrone’s solution is built entirely on open-source software, avoiding vendor lock-in.
- The platform integrates full observability, so engineers can monitor GPU utilization in real time.
- Security and access control are embedded in every layer, ensuring safe experimentation.
- GitOps management allows every change to be tracked, auditable, and reversible.
- With these improvements, the lab can now run parallel experiments efficiently, saving both time and resources.