DevOps services for data and AI workloads

We build and tidy the infrastructure and delivery pipelines behind software, data pipelines and model serving, so releases are routine, environments are reproducible and incidents are understood.

What we build

• Infrastructure as code. Networks, clusters, databases, storage and permissions defined in version control, reviewed like application code, and reproducible across environments.
• CI/CD pipelines. Build, test, scan and deploy workflows, including pipelines that handle large model artifacts and GPU test jobs.
• Containers and orchestration. Docker images that are small and reproducible, and Kubernetes or managed container services where the workload justifies them.
• GPU and batch workloads. Scheduling, autoscaling and cost controls for training and inference capacity.
• Observability. Metrics, logs and traces, with alerts tied to actions someone owns, and dashboards worth looking at.
• Security basics. Least-privilege access, secrets management, dependency and image scanning, and audit trails. For the data side, see our guide to security controls for personal data.

DevOps for models has extra needs such as artifact registries and evaluation gates, covered under MLOps; for LLM features, see LLMOps.

How an engagement runs

1. Inventory. We document what runs where, how it is deployed, who has access and what has failed recently.
2. Prioritize. Together we rank the problems by pain and risk, such as manual releases, unrecoverable servers or missing backups, and agree what to fix first.
3. Codify. Existing infrastructure is described in code, imported where needed, and rebuilt in a clean environment to prove the code is complete.
4. Automate delivery. Pipelines take a change from pull request to production, with tests, approvals where you want them, and a one-step rollback.
5. Add visibility. Monitoring, alerting and log retention, with a short on-call guide describing what each alert means.
6. Document and train. Diagrams, runbooks and a session for your engineers, so the system is not a black box.

Cost, reliability and data protection

Data and AI workloads can be expensive in quiet ways: idle GPUs, oversized instances, unbounded log volume, forgotten storage. We add tagging, budgets and alerts so spend is visible. For reliability we set backup and restore procedures and test them, since an untested backup is a hope. Where systems hold personal data, we keep environments separated, limit who can read production data, and set retention on logs and backups; background reading is in data mapping and discovery and sub-processor lists, which is relevant when infrastructure vendors process your customers' data. General information, not legal advice.

Typical stack

• Terraform or OpenTofu for infrastructure; Ansible where configuration management is still needed.
• GitHub Actions or GitLab CI for pipelines; container registries and image scanning.
• Docker, Kubernetes with Helm or Kustomize, or managed services on AWS, Google Cloud or Azure.
• Prometheus, Grafana and OpenTelemetry, or your cloud's native monitoring.
• Secrets managers, SSO and role-based access control.

Deliverables

• Infrastructure and pipeline code in your repositories.
• Documented environments with a diagram and an access list.
• Monitoring, alert rules and an on-call guide.
• Backup and restore procedures, tested once with a written result.
• A handover session for your engineers.

Working with your team

DevOps work succeeds when the people who run the system understand it, so we work in the open. Changes go through pull requests in your repositories, and we explain the reasoning in review so that decisions are not locked in our heads. We prefer a short written plan before significant changes, covering what will change, what could go wrong and how to revert.

We are comfortable working with whatever is already there, whether that is a handful of servers configured by hand, a cloud account that has grown organically, or a Kubernetes cluster that no one wants to touch. The first goal is rarely a redesign. It is to make the current system understandable and recoverable, then to improve it in steps small enough to review.

For data and AI teams specifically, we pay attention to things general-purpose DevOps guides skip: large artifacts that slow down builds, GPU capacity that is expensive to leave idle, data access permissions that are broader than anyone intended, and pipelines whose failures are discovered a week later. These are solvable with routine engineering once someone looks at them deliberately.

Frequently asked questions

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