Overview
DevOps engineering is what determines whether software releases are routine or risky, and whether infrastructure changes are automated and auditable or manual and fragile. Our Bangalore-based DevOps team builds CI/CD pipelines, infrastructure automation, and the production monitoring that keeps systems, including AI systems, reliable at scale.
DevOps Discipline for Software and AI Systems
Traditional DevOps and MLOps share a common foundation, automated pipelines, infrastructure as code, and production monitoring, but AI systems add requirements DevOps alone doesn't cover, like model drift detection and retraining automation. Our Bangalore team works across both, since most enterprise clients now need DevOps discipline applied to AI systems as much as traditional software.
This DevOps work underpins everything from cloud services & migration to MLOps & DevOps for AI systems, and connects closely with our AIOps solutions practice.
What We Deliver
CI/CD Pipeline Development
Automated build, test, and deployment pipelines that make releases routine.
Infrastructure as Code
Infrastructure defined and version-controlled, not manually configured.
MLOps & Model Deployment
Deployment and monitoring pipelines built specifically for machine learning models.
Production Monitoring
Observability across infrastructure and application health.
Benefits
- Faster, safer software releases through automated CI/CD pipelines.
- Infrastructure that's reproducible and auditable, not manually configured.
- MLOps discipline applied consistently to AI systems already in production.
- Delivery coordinated with our Atlanta team for global clients.
How We Work
Current State Assessment
Reviewing your existing deployment and infrastructure processes.
Pipeline & Infrastructure Design
Designing CI/CD pipelines and infrastructure-as-code architecture.
Implementation
Building and testing the automated pipeline and infrastructure.
Monitoring & Iteration
Setting up observability and iterating based on real operational data.
Industries We Serve
Technology & Expertise
Why XFactr.AI
This DevOps and MLOps discipline underpins the production AI work described across our case studies.
Our Bangalore Presence
Our Bangalore-based DevOps engineers work alongside our Mangalore team and Atlanta-based delivery leadership, supporting infrastructure and deployment engagements for enterprise clients globally.
Frequently Asked Questions
What's the difference between DevOps and MLOps?
DevOps focuses on automating software build, test, and deployment. MLOps applies similar automation principles specifically to machine learning models, adding requirements like drift detection and retraining that traditional DevOps doesn't cover.
Do you work with our existing CI/CD tooling, or recommend new tools?
We typically work within your existing tooling where it's a reasonable fit, recommending changes only where the current setup is a genuine constraint.
Can DevOps practices help with AI system reliability specifically?
Yes, applying DevOps-style automation and monitoring discipline to AI model deployment is a core part of making AI systems reliable in production.
What cloud platforms does your DevOps team support?
Primarily AWS and Azure, using tools like Terraform, Kubernetes, and Docker for infrastructure automation and orchestration.
How quickly can we expect to see improvement in deployment reliability?
Initial CI/CD improvements are often visible within the first few weeks; a fully mature DevOps practice typically develops over a few months.
Looking to improve deployment reliability?
We'll assess your current pipeline and infrastructure setup.
Talk to Our DevOps Team