Service details
MLOps & Infrastructure
MLOps & Infrastructure
End-to-end ML pipelines on AWS, GCP, or Azure. CI/CD for models, feature stores, experiment tracking, model registries. Built for scale and governed for compliance.
End-to-end ML pipelines on AWS, GCP, or Azure. CI/CD for models, feature stores, experiment tracking, model registries. Built for scale and governed for compliance.
Who this service is for/not for
Ideal for
Teams moving models to production, organizations managing multiple ML systems, companies needing CI/CD pipelines for ML, businesses requiring monitoring & teams building scalable ML.
Not ideal for
Teams still exploring AI ideas, orgs without trained models, simple analytics-only projects, teams lacking deployment infrastructure, or those expecting nearly zero maintenance.
Problems we solve
Pain 1: Manual copy-paste deployments workflow..
Data scientist emails pickle file. Engineer copies to server, writes Flask wrapper, prays. Updates repeat this mess.
Pain 2: Zero production visibility.
Models run as black boxes. Performance degradation discovered only when revenue drops or customers complain.
Pain 3: Manual retraining takes weeks still.
Data scientists manually pull data, re-run notebooks, validate, coordinate deployment. 2-4 weeks per iteration.
Pain 4: Can't track production model versions.
15 model versions across 3 environments. Nobody knows which is live. Debugging requires server log archaeology.
Deliverables that drive results
1.Infrastructure audit & pipeline mapping process.
Review data pipelines, workflows, and infrastructure to prepare for reliable ML operations readiness.
2.CI/CD for ML systems.
Implement automated pipelines for model training, testing, deployment, and rollback.
3.Monitoring & model governance.
Set up performance monitoring, alerting, versioning, and governance controls.
4.Scalable production infrastructure setup systems.
Deploy ML infrastructure supporting APIs, batch jobs, and real-time inference.
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FAQ
Before you get started
How is this different from large consulting firms?
What if our data is messy?
Do you only build models?
How long does it take to go live?
Do you require long-term contracts?