Most ML initiatives fail after the demo. The model works in a notebook; operators do not trust it; drift goes undetected; and GPU costs spike without anyone owning the inference stack.
At TechieYan Technologies, we treat MLOps as part of the product — not an afterthought. Here is what enterprises should demand before signing an AI engagement in India.
Demo ML vs production ML
- Demo ML: single accuracy number, no monitoring, manual retrains, opaque dependencies.
- Production ML: documented eval harness, model registry, automated retraining triggers, inference SLAs, and audit-ready lineage.
Non-negotiable MLOps deliverables
- Model cards and eval reports — sensitivity, specificity, latency percentiles, and known failure modes.
- Registry and versioning — MLflow or equivalent with promoted stages (staging → production).
- Drift monitoring — Evidently or custom dashboards that alert before operators notice.
- Inference optimisation — batching, quantisation, and hardware selection tied to cost targets.
- Rollback path — one-click revert when a new model degrades in production.
How TechieYan delivers
Our MLOps practice pairs with AI & ML development so clients get one team from training through deployment. Engagements include fixed-scope POC sprints with go/no-go gates before milestone builds.
Next step
Stalled ML initiative? We run audit-first rescue engagements. Contact TechieYan — Hyderabad, India — for a scoped proposal within days.
FAQ
What is the difference between MLOps and DevOps?
DevOps automates application deploys. MLOps adds model versioning, data validation, drift detection, and reproducible training — because ML artefacts degrade silently unlike traditional software.
How long does production ML deployment take?
From a validated POC, production hardening typically runs 4–8 weeks depending on inference SLAs, compliance requirements, and integration with existing systems.
