Oracle Cloud Infrastructure Data Science Professional — Lesson 4: Deployments, Pipelines, MLOps, Monitoring, Security, and Governance

Learning Objectives

• Explain the purpose and operating context of model artifacts, runtime environments, deployments, endpoints, and scaling.
• Apply jobs, pipelines, schedules, retraining, and CI/CD integration awareness and latency, throughput, logs, alarms, data drift, model drift, and incidents in a controlled environment.
• Validate outcomes and troubleshoot risks involving security, audit, retention, cost, ownership, retirement, and MLOps runbook.

Detailed Topics

• model artifacts, runtime environments, deployments, endpoints, and scaling
• jobs, pipelines, schedules, retraining, and CI/CD integration awareness
• latency, throughput, logs, alarms, data drift, model drift, and incidents
• security, audit, retention, cost, ownership, retirement, and MLOps runbook

Instructor Demonstration

Demonstrate model artifacts, runtime environments, deployments, endpoints, and scaling for an enterprise model portfolio designed for secure collaboration, repeatable delivery, and monitored inference; capture the exact configuration, code, command, transaction, query, dashboard, model, schedule, or report evidence that proves the expected result.

Hands-On Exercise

Complete a controlled Oracle Cloud Infrastructure Data Science Professional exercise covering jobs, pipelines, schedules, retraining, and CI/CD integration awareness and latency, throughput, logs, alarms, data drift, model drift, and incidents; introduce an exception involving security, audit, retention, cost, ownership, retirement, and MLOps runbook, diagnose it from evidence, apply the smallest reversible correction, and rerun acceptance checks.

1. Confirm scope, prerequisites, identities, dependencies, data, risks, and expected outcome.
2. Perform the course-specific configuration, development, administration, analysis, or business-process workflow.
3. Capture commands, settings, records, logs, screenshots, reports, metrics, or test results.
4. Compare the observed result with acceptance criteria and document corrective or preventive action.

Scenario Analysis

• Identify the technical or business decision and the stakeholders affected by it.
• Compare security, performance, reliability, compliance, cost, usability, and support trade-offs.
• Define rollback, recovery, communication, evidence, ownership, and escalation requirements.

Troubleshooting Guidance

Diagnose issues involving model artifacts, runtime environments, deployments, endpoints, and scaling, jobs, pipelines, schedules, retraining, and CI/CD integration awareness, latency, throughput, logs, alarms, data drift, model drift, and incidents, security, audit, retention, cost, ownership, retirement, and MLOps runbook without making an unverified production change. Separate symptoms from causes, gather time-correlated evidence, test a reversible hypothesis, validate the correction, and record recurrence controls.

Validation Checklist

• The requirement, environment, inputs, dependencies, and expected result are documented.
• The result is reproducible and supported by product-specific evidence.
• Normal, boundary, failure, security, performance, and operational conditions are reviewed.
• Exceptions, rollback, ownership, support actions, and the next improvement priority are recorded.

Lesson Outcome

Learners can apply Deployments, Pipelines, MLOps, Monitoring, Security, and Governance in a realistic Oracle Cloud Infrastructure Data Science Professional scenario and defend the result with documented evidence.