DevOps y MLOps
Pipelines de CI/CD, despliegues en contenedores, versionado de modelos y monitoreo en producción para cargas de trabajo de ML.
Infrastructure that doesn't wake you up
We build CI/CD pipelines, deployment automation, observability, and on-call readiness so your team can ship confidently without 3am pages. For ML workloads we add model versioning, drift detection, and retraining pipelines.
What we set up
GitHub Actions / GitLab CI pipelines with test gates, container builds, infrastructure-as-code (Terraform / Pulumi), Kubernetes or serverless deployment targets, secrets management, blue-green or canary rollouts, and rollback automation.
Observability that matters
Logs, metrics, traces, and error tracking wired in from day one — structured logging, dashboards that map to user journeys, alerting that triggers on symptoms users actually feel (not just CPU spikes).
MLOps specifically
For ML/AI workloads we add: model registry, experiment tracking, evaluation CI (so a model change can't ship without passing evals), feature store integration, drift detection in production, and retraining triggers. This is the difference between an ML demo and an ML product.
Preguntas frecuentes
AWS, GCP, or Azure?
All three. We default to AWS for breadth of services, GCP for ML/AI workloads (Vertex AI, BigQuery), and Azure for enterprises already invested in the Microsoft ecosystem. We meet you where you are.
Can you train our team to operate it?
Yes — we ship with runbooks, on-call playbooks, and a handover period where we pair with your team until they're confident owning the system. The goal is always to make ourselves unnecessary.
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