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MissionLayer SystemsUtah Small Business

Capability

Cloud, DevOps, and MLOps

The production layer under the applications above: infrastructure that can be rebuilt from source, deployments that are routine, and models that are watched.
Discuss a RequirementCapability Statement

The problem

Cloud environments assembled under deadline pressure share the same problems: manual configuration nobody can reproduce, permissions broader than they need to be, unclear cost drivers, and no reliable path back after a failure. Deployment that is manual is rare, and rare deployment means each release carries more change and more risk. Machine learning adds a third problem — models drift, and without monitoring the first signal is a user complaint.

Typical technologies

  • Kubernetes
  • Docker
  • AWS
  • Terraform
  • Helm
  • GitHub Actions
  • ArgoCD
  • Prometheus and Grafana
  • OpenTelemetry
  • MLflow
  • Linux

What we deliver

  • Cloud architecture on AWS and other cloud-native platforms
  • Kubernetes cluster architecture, workload design, and day-two operations
  • Containerized deployment with reproducible builds
  • Infrastructure as code, so environments are rebuilt rather than repaired by hand
  • CI/CD pipelines covering build, test, security scanning, and deployment
  • DevSecOps practice: least-privilege identity, secret management, dependency scanning in the pipeline
  • Model deployment, model serving, versioning, and experiment tracking
  • Monitoring and observability — metrics, structured logs, tracing, alerting
  • Data and model drift monitoring with defined thresholds
  • Backup, recovery, environment separation, and rehearsed rollback
  • Performance tuning and cost optimization based on measured usage

Examples of project types

These describe the kind of work we take on. They are illustrative scopes, not descriptions of completed client projects.

  • A Kubernetes environment defined in code, with namespaces, resource limits, ingress, secret handling, and documented runbooks
  • A pipeline that takes a change from commit to a tested deployment without manual steps
  • A model-serving layer with versioned endpoints, load-appropriate scaling, and prediction logging
  • An observability baseline that answers "is it working, is it slow, and what changed" from one place
  • An infrastructure assessment identifying cost drivers, single points of failure, and configuration that exists only in the console

Expected outcome

Infrastructure that can be rebuilt from source control, explained to an auditor, and handed to another team without a long oral history — and deployments small enough that releasing stops being an event.

Built by us

Products that show this work

Company-developed software that uses this capability — demonstrable on request, and not contract past performance.

Questions

Cloud, DevOps, and MLOps: common questions

Do you set up and run Kubernetes environments?
Yes — cluster architecture, workload and namespace design, ingress and secret handling, autoscaling, monitoring, and runbooks, for new environments or existing ones that need assessment and repair.
What does MLOps include?
Versioned models and data, repeatable training and evaluation, deployment pipelines, monitoring for drift and cost, and a rollback path — the practices that keep a model reliable after launch.
Do you build security into the delivery pipeline?
Yes: dependency and static analysis, secret scanning, signed and pinned artifacts, least-privilege access, and audit logging are part of the pipeline rather than a review at the end.

Other capabilities

Next step

A requirement for Cloud, DevOps, and MLOps?

Send the solicitation number or a short description of the need and its deadline. We will tell you plainly whether we are the right fit and what a first step would look like.
Share a SolicitationRequest a Capability Discussion

Or email contact@missionlayersystems.com or call (949) 501-1291.