This tutorial walks through one full cycle of the platform, from start to finish, using a detection model that finds whether people wear safety helmets as the example. Done in order, it takes about 30 minutes.

Sign in → Dataset → New training (11 steps) → Watch progress → Compare runs, register model
      → Production promotion request → Approval → Serving image, API service → Edge deployment
      → Monitoring, drift → Retrain

What you need

  • The platform address and an account. The screenshots in this tutorial were taken in the tenant DEMO.
  • Each step needs a different role. If one person has the admin role, they can do everything; if roles are split, hand off to a colleague with the right role.
PageWhat you doRole required
1. Sign-in and tenantSign in, check the tenantAnyone
2. Create a datasetCreate a dataset, upload filesDeveloper, operator, admin
3. New trainingStart training with the 11-step wizardDeveloper, operator, admin
4. Watch training progressSix-step timeline, MLflow metricsAnyone
5. Compare runs and register a modelCompare runs, register in the model registryRegistering: developer, operator, admin
6. Promotion request and approvalProduction promotion request, approvalApprover, admin
7. ServingContainer image build, API service, inference consoleOperator, admin
8. Edge deploymentDeployment record, sending a model to a deviceOperator, admin
9. Monitoring and retrainingMonitoring, drift, retrainingViewing: anyone; retraining: developer or higher

If you split the tutorial by role, do it like this:

  • Developer: 1 → 5 (dataset, training, registration)
  • Approver: 6 (approval)
  • Operator: 7 → 9 (serving, deployment, operations)

When you are done, look up common tasks (stopping and resuming training, rollback, silencing alerts and more) in How-to.

Written for the platform as of 2026-09-21.

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