Oracle Cloud Infrastructure Data Science Professional: Hands-On Workshop — Lesson 3: Model Training, Evaluation, Tuning, Explainability, and Cataloging

Learning Objectives

• Explain the purpose and operating context of classification, regression, forecasting, and algorithm selection.
• Apply metrics, cross-validation, imbalance, thresholds, and error analysis and hyperparameter tuning, AutoML awareness, experiments, and comparison in a controlled environment.
• Validate outcomes and troubleshoot risks involving explainability, bias review, model catalog metadata, and approvals.

Detailed Topics

• classification, regression, forecasting, and algorithm selection
• metrics, cross-validation, imbalance, thresholds, and error analysis
• hyperparameter tuning, AutoML awareness, experiments, and comparison
• explainability, bias review, model catalog metadata, and approvals

Instructor Demonstration

Demonstrate classification, regression, forecasting, and algorithm selection for a customer-churn model developed in OCI Data Science and promoted through a controlled deployment; 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: Hands-On Workshop exercise covering metrics, cross-validation, imbalance, thresholds, and error analysis and hyperparameter tuning, AutoML awareness, experiments, and comparison; introduce an exception involving explainability, bias review, model catalog metadata, and approvals, 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 classification, regression, forecasting, and algorithm selection, metrics, cross-validation, imbalance, thresholds, and error analysis, hyperparameter tuning, AutoML awareness, experiments, and comparison, explainability, bias review, model catalog metadata, and approvals 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 Model Training, Evaluation, Tuning, Explainability, and Cataloging in a realistic Oracle Cloud Infrastructure Data Science Professional: Hands-On Workshop scenario and defend the result with documented evidence.