All services

DevOps and MLOps

CI/CD, infrastructure as code, and the pipelines that ship models and agents.

Plan an engagement

We make shipping routine for both software and AI: pipelines that test and scan every change, infrastructure reviewed like code, and MLOps and LLMOps practices that version, evaluate and roll out models and agents as safely as any release.

Pipeline · support-assistantExample
  1. Checkoutpassed
  2. Tests and scanspassed
  3. Build imagepassed
  4. Evaluate modeleval gate passed
  5. Canary deploy25% traffic
  6. Promotewaiting

What it means in practice

  • CI/CD pipelines with automated tests and security scans, on GitHub Actions, GitLab or AWS CodePipeline.
  • Infrastructure as code with Terraform, AWS CDK and CloudFormation, with review, plans and drift detection.
  • Release strategies: blue/green, canary and feature flags, with fast rollback.
  • MLOps on Amazon SageMaker AI: training and deployment pipelines, a model registry, managed MLflow experiment tracking and drift monitoring.
  • LLMOps on Amazon Bedrock: prompt and model versioning, Guardrails, and evaluation gates before every release.
  • AgentOps on Amazon Bedrock AgentCore: agents deployed with built-in identity, policy controls, evaluations and observability.
  • AI-assisted operations with AWS DevOps Agent for incident investigation and release-readiness checks, and Kiro for spec-driven development.

What you get

  • Pipelines for your services, models and agents
  • Reusable infrastructure-as-code modules
  • A release process with evaluation gates and rollback, for code and AI
  • Documentation and team training

Typical stack

  • GitHub Actions
  • GitLab CI
  • AWS CodePipeline
  • Terraform
  • AWS CDK
  • Argo CD
  • Amazon SageMaker AI
  • MLflow
  • Amazon Bedrock
  • Bedrock AgentCore
  • AWS DevOps Agent
  • Kiro

We work with the tools your teams already use.

How it runs

  1. AssessWe review your architecture, data, goals and constraints, and agree what success looks like.
  2. DesignTarget architecture, a delivery plan and the measures we will be judged on.
  3. BuildShort iterations, working software early, built alongside your team in your accounts.
  4. RunWe operate it to agreed service levels, improve it, or hand it over with runbooks.
More on how we work

Tell us where your AI is stuck.

One conversation with an engineer. No sales script.

Plan an engagementhello@cloudernetes.com