deployService 13
MLOps & AI Production Operations
Keep your AI running, improving, and compliant — day after day, model after model.
40% lower inference costs
Only 1% of enterprises feel they’ve achieved true AI maturity. Inference spending surpassed training for the first time in 2026 — proving companies run AI at scale and need operational frameworks.
MLOps ensures models deliver reliable, governed, cost-efficient value in production, not just notebooks.
What we deliver
- CI/CD pipelines for ML: automated training, validation, testing, deployment
- Model registry and versioning with lifecycle tracking
- Production monitoring: performance, data drift, concept drift, latency
- Automated retraining with human approval gates and rollback
- LLMOps: prompt versioning, response quality monitoring, cost/token tracking
- A/B testing, canary deployment, blue-green strategies
- GPU/inference optimisation: cost management, autoscaling