MLOps & AI Infrastructure

Infrastructure for shipping models to production and keeping them there.

What's included

• Training and inference pipelines that don't fall over under real load
• Model versioning, rollback, and A/B rollout support
• GPU infrastructure sized and cost-controlled for actual usage patterns
• Monitoring for model drift and data quality, not just uptime
• Integration with your existing CI/CD and data infrastructure

Who it's for

Teams moving ML from notebooks into a production system that other services depend on.