Azure, CI/CD, Kubernetes & MLOps
DevOps & Cloud
Production AI requires production-grade infrastructure. We build the Azure cloud architecture, CI/CD pipelines, and MLOps platforms that let your AI systems ship faster, scale reliably, and operate without manual intervention.
Challenges We Solve
Sound Familiar?
- ML models that work in notebooks but have no path to production
- Manual deployments creating inconsistency between environments
- No rollback capability when model performance degrades
- Kubernetes clusters running at 90% cost with 30% utilization
- Development teams blocked on environment provisioning for weeks
Our Approach
How We Help
Azure Cloud Architecture
Landing zone design, AKS cluster setup, networking, security, and cost management for enterprise Azure workloads.
CI/CD Pipeline Engineering
GitHub Actions pipelines for automated testing, container builds, model validation, and environment-specific deployments with approval gates.
MLOps Platform
End-to-end MLOps on Azure ML: experiment tracking, model registry, automated retraining, A/B deployment, and drift monitoring.
Infrastructure as Code
Fully Terraform-managed Azure infrastructure — reproducible, version-controlled, and auditable from development through production.
Tech Stack
Technologies We Use
How We Work
Delivery Process
Infrastructure Audit
Review current Azure setup, cost allocation, security posture, and DevOps maturity against target state.
Target Architecture Design
Design the landing zone, AKS topology, network security groups, and environment strategy (dev/staging/prod).
IaC Implementation
Implement all infrastructure as Terraform modules with state management in Azure Storage and peer review process.
CI/CD Pipeline Setup
Build GitHub Actions workflows for all application and ML workloads with automated testing and progressive delivery.
MLOps Integration
Connect ML pipelines to the CI/CD system: automated model evaluation, registry promotion gates, and canary deployments.
Observability & Runbooks
Set up Azure Monitor, Application Insights, and PagerDuty alerting. Document on-call runbooks for all failure scenarios.
What You Get
Deliverables
Every engagement has a defined scope and concrete outputs. No vague “consulting reports” — you get production-ready artifacts.
- Azure landing zone (Terraform-managed)
- AKS cluster with autoscaling and cost optimization
- CI/CD pipelines for all application and ML workloads
- MLOps platform (Azure ML with model registry and monitoring)
- Observability stack (Azure Monitor + Application Insights + alerting)
- IaC repository with documentation and contributor guide
Why StarkLogik
What Makes Us Different
ML-Aware Infrastructure
We build infrastructure for AI workloads specifically — GPU node pools, model serving autoscaling, feature store integration, and ML experiment storage are first-class concerns.
Cost Engineering Included
Every infrastructure design comes with a cost model and optimization plan. We typically reduce cloud spend by 30–50% when taking over existing environments.
Security by Default
Private endpoints, managed identity everywhere, no public storage accounts, network segmentation, and Azure Policy enforcement are non-negotiable defaults — not add-ons.
FAQs
Common Questions
Get Started
Ready to Get Started with DevOps & Cloud?
Book a free 30-minute call with our engineering team to discuss your use case.