Oracle Cloud Infrastructure Data Science Professional (2025): Hands-on Workshop — Lesson 4: Jobs, pipelines, schedules, MLOps automation, drift monitoring, logging, cost management, troubleshooting, and professional exam scenarios

Learning Objectives

• Break down the roles, prerequisites, records, and decision points involved in Jobs, pipelines, schedules, MLOps automation, drift monitoring, logging, cost management, troubleshooting, and professional exam scenarios.
• Complete a controlled activity for Jobs, pipelines, schedules, MLOps automation, drift monitoring, logging, cost management, troubleshooting, and professional exam scenarios and capture automated ML workflow and operational monitoring plan.
• Challenge the result with a scenario involving a scheduled retraining pipeline publishing a weaker model without a promotion gate, then justify the corrective action.

Detailed Topics

• Jobs
• Pipelines
• Schedules
• MLOps automation
• Drift monitoring
• Logging
• Cost management
• Troubleshooting
• And professional exam scenarios
• Decision ownership, prerequisites, and acceptance evidence for Jobs, pipelines, schedules, MLOps automation, drift monitoring, logging, cost management, troubleshooting, and professional exam scenarios
• Relevant access, data, integration, performance, recovery, and support considerations

Instructor Demonstration

Using realistic data and role assignments, the instructor demonstrates Jobs, pipelines, schedules, MLOps automation, drift monitoring, logging, cost management, troubleshooting, and professional exam scenarios. Key checkpoints are captured in automated ML workflow and operational monitoring plan, with discussion of alternatives, side effects, and the conditions required for an acceptable result.

Hands-On Exercise

Configure and test Jobs, pipelines, schedules, MLOps automation, drift monitoring, logging, cost management, troubleshooting, and professional exam scenarios with representative records. Use “a scheduled retraining pipeline publishing a weaker model without a promotion gate” as the troubleshooting challenge, compare competing hypotheses, retest only the affected path, and complete automated ML workflow and operational monitoring plan for peer review.

1. Record the starting state, assumptions, identities, data, and expected outcome.
2. Execute the planned workflow and collect evidence at each material checkpoint.
3. Test one valid path, one boundary condition, and the stated failure scenario.
4. Document the correction, retest result, rollback option, and follow-up owner.

Scenario Analysis

• Clarify the business trigger, affected roles, required records, and acceptable result for Jobs, pipelines, schedules, MLOps automation, drift monitoring, logging, cost management, troubleshooting, and professional exam scenarios.
• Compare at least two implementation or correction choices and identify which evidence would distinguish them.
• Decide how automated ML workflow and operational monitoring plan should be reviewed, approved, retained, and handed to the next owner.

Troubleshooting Guidance

Diagnose Jobs, pipelines, schedules, MLOps automation, drift monitoring, logging, cost management, troubleshooting, and professional exam scenarios by checking effective settings, ownership, source records, dependencies, and recent releases. Use automated ML workflow and operational monitoring plan to locate the earliest divergence. Treat “a scheduled retraining pipeline publishing a weaker model without a promotion gate” as a hypothesis until the same test passes after correction and still passes for a boundary case.

Validation Checklist

• The scope of Jobs, pipelines, schedules, MLOps automation, drift monitoring, logging, cost management, troubleshooting, and professional exam scenarios is clear, including what is deliberately excluded from the test.
• Evidence in automated ML workflow and operational monitoring plan supports each material decision and outcome.
• A negative test demonstrates that controls reject or flag an invalid condition.
• Open risks, owners, due actions, and production safeguards are documented.

Lesson Outcome

Learners can apply Jobs, pipelines, schedules, MLOps automation, drift monitoring, logging, cost management, troubleshooting, and professional exam scenarios, explain the decisions made, and defend the result with automated ML workflow and operational monitoring plan.