Neuralon
Year:
2025
Service:
ML Infrastructure Automation
Industry:
SaaS
Size:
20–30 employees
Client Website:
Neuralon's data scientists were drowning in DevOps work. Source automated their entire ML pipeline — cutting release cycles by 60% and freeing researchers to do what they're actually paid for: push the boundaries of AI.
Introduction
Neuralon builds bleeding-edge AI models for computer vision and natural language processing. Their algorithms power everything from medical imaging diagnostics to real-time translation systems. The science? World-class. The infrastructure managing it? A nightmare.
Every model deployment felt like defusing a bomb. Data scientists — people with PhDs who should be solving hard research problems — were instead wrestling with Kubernetes configs, debugging version conflicts, and manually shepherding models through staging environments. Training a new model meant submitting tickets, waiting for GPU allocation, then babysitting the process for hours.
The irony was brutal: a company building intelligent systems was running on spectacularly unintelligent operations.
Challenge
Here's what deploying a single model looked like at Neuralon:
Day 1: Data scientist submits deployment request
Day 2-3: DevOps team provisions infrastructure
Day 4: First deployment attempt fails due to dependency conflicts
Day 5: Model finally deploys but performance metrics aren't tracking
Day 6-7: Debugging, rollback, repeat
Multiply that across dozens of models, multiple environments (dev, staging, prod), and constant iteration cycles. The bottleneck wasn't research velocity — it was operational friction.
The real problems:
No standardization — every team had their own deployment process
Manual everything — scaling GPU clusters, version control, performance monitoring all required human intervention
Broken feedback loops — by the time models reached production, the research context was already stale
Talent misallocation — researchers spending 40% of their time on infrastructure instead of innovation
Neuralon was hiring some of the brightest minds in AI and then burying them in DevOps busywork.
Solution
Source built an MLOps platform that treated model deployment like modern software deployment: automated, tested, and boring (in the best way).
The new system:
Automated the entire pipeline from training → testing → deployment with zero manual handoffs
Intelligently scaled infrastructure — spinning up GPU clusters when needed, shutting them down when idle
Version-controlled everything — models, data, configs, and results automatically tracked and reproducible
Built-in compliance and monitoring — every model deployment came with automatic logging, performance tracking, and rollback capabilities
Most importantly: the system was designed for researchers, not DevOps engineers. Scientists could trigger deployments with a single command, monitor training runs from a clean dashboard, and iterate on models without ever leaving their development environment.
Result
Six months in, the transformation was obvious:
60% faster model release cycles (weeks → days)
40% lower infrastructure costs through intelligent auto-scaling
Zero production incidents from deployment issues
Research teams spending 90% of their time on research — not operations
But the real win was cultural. Neuralon's weekly research meetings shifted from "why is deployment taking so long?" to "what should we try next?" The company went from shipping 2-3 models per quarter to deploying updates weekly.
One senior researcher put it simply: "I finally get to do the job I was hired for."
