MLOps
Automated drift detection, validation pipelines, and release governance.
What is this service?
Build deployment infrastructure, monitoring loops, and regression safeguards for reliable production AI operations.
Who is this for?
ML engineering departments needing to maintain high model reliability, detect drift, and govern production models.
Pricing & Engagement
- Starting from: ₹25,00,000
- Typical range: ₹25,00,000 - ₹60,00,000
- Pricing Model: Architecture design and implementation
- Engagement timeline: 4 to 10 weeks
Typical Problems We Resolve
Silent accuracy decay in production models due to changing user distributions.
Lack of rollback and deployment safety checks for newly trained models.
No automated alert pipeline when model latency or error rates spike.
Key Deliverables
Technology Stack
Expected Outcomes
Automated drift alerts flagging data distribution shifts within 1 hour.
Safe, zero-downtime canary rollouts for updated model versions.
Unified registry tracking model metrics and metadata.
Next Steps & Engagement
Arrange a technical review of your current ML model deployment logs and CI/CD pipelines.