DevOps & MLOps for Modern Teams
Ship software and ML models faster with confidence. We implement the automation, observability, and operational practices that let your team focus on building features instead of fighting deployments.
Our Approach
Automate Everything, Observe Everything
Automation First
If you're doing it manually more than twice, we automate it. CI/CD pipelines, infrastructure provisioning, model training, and deployment - all codified and repeatable.
Observability as a Foundation
Metrics, logs, and traces from day one. You can't improve what you can't measure, and you can't debug what you can't see.
MLOps for Production AI
ML models need different operational practices than traditional software. We implement proper model versioning, A/B testing, monitoring for drift, and automated retraining pipelines.
Platform Engineering Mindset
We build internal developer platforms that make the right thing the easy thing. Golden paths, self-service infrastructure, and guardrails that enable rather than restrict.
Key Offerings
DevOps & MLOps Services
From CI/CD basics to sophisticated ML deployment pipelines.
CI/CD Pipeline Implementation
GitHub Actions, GitLab CI, Azure DevOps, or Jenkins. Automated testing, security scanning, and deployment pipelines that give you confidence to ship.
Infrastructure as Code
Terraform, Pulumi, CDK, or Bicep. Version-controlled infrastructure with proper state management, drift detection, and policy enforcement.
ML Model Deployment
Model serving with proper versioning, canary deployments, and rollback capabilities. We use MLflow, Kubeflow, SageMaker, or custom solutions based on your needs.
ML Monitoring & Observability
Track model performance, detect data drift, and monitor for degradation. Automated alerts when your models start underperforming.
Container & Kubernetes Operations
Container orchestration, service mesh, GitOps workflows, and the operational practices that keep Kubernetes clusters healthy.
Observability Stack
Prometheus, Grafana, OpenTelemetry, Datadog, or cloud-native solutions. Unified observability across your entire stack.
Benefits
Ship Faster, Sleep Better
Faster Deployments
Go from code commit to production in minutes, not days. Automated pipelines remove the friction from shipping.
Fewer Production Incidents
Automated testing, canary deployments, and proper observability catch issues before they impact users.
ML Models That Stay Healthy
Monitoring and automated retraining keep your AI systems performing well over time, not just at launch.
Developer Productivity
Self-service infrastructure and golden paths let developers focus on features instead of fighting tooling.
Ready to Level Up Your Operations?
Let's discuss your DevOps and MLOps challenges - whether you're starting from scratch or optimizing existing practices.