User guide
Build and run models in the workspace, from datasets to field deployment and retraining
This chapter covers what workspace users do: upload data, train, compare results and pick a model, get approval, serve, deploy to field devices (edge), and retrain.
Installation covers installing the platform, Administration covers admin screens such as members, tokens and training runtimes, and Integration covers external training containers and direct MLflow integration.
How to read this chapter
| What | When to read it | Where |
|---|---|---|
| Tutorial — ship your first model to the field | Once, the first time, following it in order (about 30 minutes) | Tutorial |
| Screen reference | When you want to know what a button or field on a screen does | The per-screen pages of this chapter |
| How-to | When you do one common task | How-to |
Workspace menu
After you sign in, the workspace menu appears in the left sidebar. Features turned off for the tenant and menus your role cannot use are hidden (for example, Approvals (승인) is shown only to approvers and admins).
| Menu | What it does | Place in the flow |
|---|---|---|
| Dashboard (대시보드) | Summary of key metrics (KPI), recent alerts, model status, GPU slots, Edge Fleet and recent deployments | Overall |
| Experiments (실험) | MLflow experiments and run lists, run comparison, registering a run as a model | ③ Experiments |
| Training (학습) | New training (11-step wizard), training run list and detail, pretrained weights | ② Training |
| Model Registry (모델 레지스트리) | Model versions and stages (Staging, Production, Archived), production promotion requests, serving image builds | ④ Registry |
| Container Registry (컨테이너 레지스트리) | Browse and import images in the platform's built-in registry | Supporting |
| Pipelines (파이프라인) | Pipeline run history | Supporting |
| Deployments (배포) | Record which model version went to which device, record rollbacks | ⑦ Deployment |
| API Services (API 서비스) | Create serving endpoints, test them in the inference console | ⑥ Serving |
| Monitoring (모니터링) | Operational KPIs and trends | ⑨ Monitoring |
| Alerts (경보) | Handle active alerts; rules, channels and silences | ⑨ Monitoring |
| Approvals (승인) | Approve or reject production promotion requests | ⑤ Approval |
| Datasets (데이터셋) | Create datasets, upload files, sync external sources | ① Datasets |
| Edge Fleet (Edge Fleet) | Field device status, commands (pull model) and policies | ⑧ Edge |
| Collected data (수집 데이터) | Find and clean up collected data (records, inference results, files) uploaded by all devices in the tenant | ⑧ Edge |
| Drift (드리프트) | Trends in input data distribution shift (PSI, KS), retraining | ⑩ Drift → retraining |
At the top right are the Tenant selector (테넌트 선택) (for example DEMO), the notification bell and the account menu. If you have admin permission, Admin console (관리자 콘솔) also appears at the bottom of the sidebar.
The whole flow
A model goes through the steps below in order. When drift is detected, it goes back to ② Training and the steps repeat.
| Step | Menu | Role required | Result |
|---|---|---|---|
| ① Create a dataset, upload files | Datasets | Developer, operator, admin | A FROZEN dataset and its file list |
| ② Training | Training → New training | Developer, operator, admin | One training run = one MLflow run |
| ③ Compare runs | Experiments | Anyone (view) | The chosen run |
| ④ Register the model | Experiments → Run → Register model | Developer, operator, admin | A model version (Staging) |
| ⑤ Production promotion request → approval | Model Registry → Approvals | Approver, admin | A Production version |
| ⑥ Serving image build, API service | Model Registry → API Services | Operator, admin | Container image, inference endpoint |
| ⑦–⑧ Deployment and edge devices | Deployments, Edge Fleet → Commands | Operator, admin | A deployment record, a model running on the device |
| ⑨–⑩ Monitoring, drift | Monitoring, Alerts, Drift | Anyone (view) | Retraining decision |
The tenant admin sets a role for each member. There are five: viewer, developer, operator, approver and admin (Roles and permissions). Buttons you lack permission for are hidden, and the server rejects the request too. A button that is visible but disabled is unavailable because of the current state, not your permission (for example Register model (모델 등록) on a run that has not finished).