Tech team · MLOps

MLOps: production models on Kubernetes, not snowflake clusters

MLOps sits between ML code and platform reality. FusioNative gives you a repeatable deploy path for models, GPU allocation you can defend in review, and monitoring that speaks both inference and infrastructure.

Deploy and monitor models on Kubernetes with guardrails

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Take models from artifact to monitored production services with wizard-driven deploys and fleet-grade observability.

Who needs this

  • MLOps engineers owning the path from registry to production
  • Inference platform teams standardizing deploy templates
  • Reliability partners pairing models with SLO dashboards

Industry pressures (why change)

  • Each team hand-rolls Helm for models—drift breaks reproducibility
  • GPU requests do not match real pool capacity
  • Inference outages lack ties between model metrics and node health

Why FusioNative fits

  • LLM deploy wizard covers cluster through service exposure
  • GPU and workload screens align inference with node capacity
  • GitOps and registry integrations complete the delivery loop

How teams adopt it

  1. Step 1. Standardize on wizard or GitOps paths for model rollouts
  2. Step 2. Validate GPU and storage quotas per environment before promote
  3. Step 3. Monitor inference KPIs alongside pod restarts and events
  4. Step 4. Document rollback using the same deployment inventory
In Cloud Admin

What MLOps teams see in the product

Real screens—how and why each view matters for your sector.

Deploy · Cluster
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Deploy · Cluster

First wizard step—anchor the model to the right cluster and policy boundary.

  • Validated cluster pick
  • Policy-aware progression
  • Fewer wrong-environment deploys

Click to zoom and pan the screenshot.

Deploy · Resources
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Deploy · Resources

GPU and CPU allocation step—align requests with real pool headroom.

  • Quota visibility
  • GPU pool alignment
  • Blocks oversubscription mistakes

Click to zoom and pan the screenshot.

Deployed models overview
03 of 03 Cloud Admin

Deployed models overview

Post-deploy KPIs—operations view after go-live.

  • Inference metrics
  • Model inventory
  • Bridge-friendly summaries

Click to zoom and pan the screenshot.

Go deeper

Related product areas

Features

On-prem LLM Models · AI Workloads · Container Registry · GitOps Deployments

Use cases

LLM & GPU operations · GitOps & platform engineering