Cloud Engineering, DevOps, MLOps and LLMOps

Short answer. AgenticNex AI engineers the cloud and operations layer that keeps data and AI systems reliable: infrastructure as code, CI/CD, Kubernetes, observability, MLOps for model training and deployment, and LLMOps for evaluation and monitoring of language-model applications.

What is the business problem?

AI systems built without operational engineering are deployed by hand, unmonitored and hard to update, so they degrade quietly after go-live.

Why can conventional approaches be insufficient?

  • Manual deployments are slow and error-prone.
  • Models are not versioned with their data and code.
  • No drift, cost or quality monitoring for models and LLMs.

What is our engineering approach?

  1. Codify infrastructure and environments.
  2. Automate build, test and deployment pipelines for apps and models.
  3. Version data, features and models together.
  4. Monitor performance, drift, cost and quality in production.

What does the architecture look like?

Code · data · models in GitCI/CD pipelinesModel registryKubernetes / serverlessObservabilityDrift & quality monitoringRetraining loop
Cloud / DevOps / MLOps reference architecture. Code · data · models in Git → CI/CD pipelines → Model registry → Kubernetes / serverless → Observability → Drift & quality monitoring → Retraining loop

Which technology capabilities are involved?

  • Terraform / Bicep
  • GitHub Actions / Azure DevOps
  • Kubernetes and Helm
  • MLflow-class registries
  • Prometheus / Grafana / OpenTelemetry
  • LLM evaluation and tracing

Example use cases

  • Migration of AI workloads to Azure or AWS
  • MLOps platform for a data science team
  • LLMOps for GenAI assistants
  • Hybrid cloud with edge deployment

Expected business and technical outcomes

  • Repeatable, auditable deployments
  • Faster, safer model updates
  • Visibility into model quality and cost
  • [Insert verified project result]

Relevant case studies

Case study details coming soon.

Frequently asked questions

What is the difference between MLOps and LLMOps?

MLOps manages training, deployment and drift of predictive models. LLMOps adds prompt and retrieval versioning, evaluation sets and safety monitoring for language-model applications.

Do you work on Azure and AWS?

Yes, as well as on-premise Kubernetes and air-gapped environments.

Related insights

Digital Transformation

How to Build an Enterprise GenAI Platform

An enterprise GenAI platform provides shared services that every GenAI application needs: document ingestion, hybrid retrieval, model access through a gateway, permission enforcement, guardrails, evaluation and monitoring.

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