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AI Models (model serving)

What it's for

Deploy a trained model (from S3, an MLflow registry, or your own container image) as a live inference endpoint — with canary rollout, an API key, and a built-in playground to test it — without hand-writing Kubernetes/KServe manifests.

How to deploy a model

  1. Go to Models in the project nav → Deploy model.
  2. Fill in:
    • Name (lowercase/digits/hyphens, e.g. iris-classifier).
    • Model type — sklearn / xgboost / lightgbm / pytorch / tensorflow / triton / huggingface / custom.
    • Source:
      • S3 — an Artifact URI (e.g. s3://platform-models/<project>/iris/v1).
      • MLflow — a registered model Name + Version.
      • Custom — a Container image you built yourself.
    • Profile — cpu-small / cpu-medium / gpu-t4 / gpu-a100 (shows CPU/RAM/GPU count per option).
    • Auth mode — apikey / jwt / public.
    • Attached app and Version label (both optional).
  3. If you pick a GPU profile and your project's GPU quota is currently 0, the form warns you the deploy will need admin approval — see AI model approvals.
  4. Submit — you're redirected to Operations to watch it deploy.

Deploying straight from your MLflow registry

Instead of typing a name/version by hand, go to the account menu (top-right avatar) → AI Studio. This is a cross-project registry browser: pick a project, see every registered MLflow model with its latest version/stage/updated time, and click Deploy vN — it opens the Models page with the create form pre-filled (source, name, version) for that exact version.

Managing a deployed model

Open a model card to reach its detail page — tabs Overview / Versions / Access / Playground / Operations, plus Manifests for Owner/Admin.

  • Set Canary (top of the page) — drag 0–100% to send a slice of traffic to a newly pinned version. Once canary is above 0%, a Promote button appears to make it the stable 100% version.
  • Versions tab — push a new artifact without redeploying: paste a new S3 URI, or pin a different MLflow version.
  • Access tab — see the auth mode and (for apikey mode) click Reveal to show the full API key in a highlighted box with a "save it now" warning.
  • Playground tab — send a live test request to the model straight from the console.
  • Delete (top of the page) — has a Force checkbox for models stuck in a bad state.

Gotchas

  • GPU profiles can silently need approval. If your project's GPU quota is 0, deploying on gpu-t4/gpu-a100 creates a request that sits pending until an Owner/Admin approves it in AI model approvals — the deploy doesn't fail, it just doesn't go live yet.
  • Revealing the API key is not visually marked one-time-only — the button can be clicked again in a fresh page load; still treat it as sensitive and copy it immediately rather than leaving the tab open.
  • Manifests tab is Owner/Admin only — Developers can deploy, canary, promote, and delete, but can't see the raw resolved KServe spec or GitOps commit/Argo Application details.
  • The inference call counter on the project's quota cards is advisory only (no hard cutoff shown in the UI) — CPU/GPU model count quotas are the ones that actually block deploys.

Not yet supported

  • Editing model type/profile/auth mode after creation (only artifact/version updates, canary %, and delete).
  • A/B testing beyond simple canary percentage (no multi-version traffic splitting).
  • Deleting without at least considering Force for stuck models — there's no separate "diagnose why it's stuck" tool in the UI.
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