DevOps

What is DevOps?

DevOps is a cultural and technical movement that breaks down the traditional wall between software development (Dev) and IT operations (Ops). It emphasises automation, continuous integration, continuous delivery, monitoring, and rapid feedback loops to ship reliable software faster.

The term emerged around 2009, but the practices have matured into a foundational discipline that underpins modern software delivery at every scale.

Core Practices

• CI/CD — Automated build, test, and deployment pipelines (GitHub Actions, GitLab CI, Jenkins)
• Infrastructure as Code — Define infrastructure in version-controlled templates (Terraform, Pulumi, CloudFormation)
• Containerisation — Package applications in portable containers (Docker, Podman)
• Orchestration — Manage containerised workloads at scale (Kubernetes, ECS)
• Monitoring & Observability — Track application health, performance, and errors (Datadog, Grafana, Prometheus)
• GitOps — Use Git as the single source of truth for both code and infrastructure

MLOps: DevOps for AI

The rise of AI has spawned MLOps—DevOps principles applied to machine learning workflows. This includes model versioning, experiment tracking, data pipeline orchestration, model serving, and drift monitoring. Tools like MLflow, Weights & Biases, and DVC have become essential.

The Blue Note Logic Perspective

We practice what we preach: every CorpusAI deployment ships with full CI/CD pipelines, infrastructure as code, and automated model evaluation. Our clients often come to us with AI models running on someone's laptop—we help them build the MLOps foundation to run those models in production with confidence. The bridge from prototype to production is where most AI projects fail, and DevOps discipline is what gets you across.