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Beginner60 to 120 minutesInstructor visible
AI Engineer Career Path Beginner lab 1
A learner provisions an isolated ai engineer career path environment and completes a guided operational task.
Business context
Ultiblob uses this exercise to train ai engineer and applied ml engineer candidates on realistic private-cloud lab operations rather than static videos.
Technical objective
Configure the core ai engineer career path services, verify health, and record the result in the lab progress view.
Student instructions
- 1Open the lab workspace and review the topology map.
- 2Launch the required templates and wait for all provisioning checks to complete.
- 3Complete the configuration task in the course module.
- 4Run validation and capture the result for instructor review.
- 5Create a snapshot before any risky troubleshooting or failure exercise.
Troubleshooting
- If access fails, confirm the bastion session is active and the instance is not expired.
- If validation fails, inspect the lab event log before rerunning the check.
- If configuration drifts, restore the latest clean snapshot and repeat the module task.
Cleanup
- Export notes or reports required by the instructor.
- Restore or delete temporary snapshots created during the exercise.
- Use the teardown action when the module is complete or allow the TTL policy to expire the lab.
Required templates
- Python data science workstation - defined
- Vector database/RAG node - defined
- Ollama/LiteLLM client node - defined
Validation checks
- Notebook reachable: JupyterLab returns a valid login page or authenticated health response.
- AI endpoint reachable: The model gateway returns at least one allowed model for the tenant.
Expected result
The lab reaches Healthy state for Notebook reachable, AI endpoint reachable.
Reset policy: Student can reset to the last clean snapshot; instructor can force reset from admin view. Teardown policy: Automatic teardown at TTL expiry with manual instructor override for cohorts.