Deepdive Into Machine Learning Using Autonomous Database — Lesson 2: Oracle Machine Learning Algorithms and Model Building

Technical Scope

• classification, regression, clustering, anomaly detection, and association choices
• OML SQL and notebook workflows for model creation
• algorithm settings, sampling, class imbalance, and model metadata
• experiment naming, parameter capture, repeatability, and model inventory

Instructor Demonstration

The instructor will configure and verify classification, regression, clustering, anomaly detection, and association choices in the context of a customer-retention use case where sensitive warehouse data must be prepared, modeled, explained, and scored without unnecessary movement. The demonstration captures commands, configuration, transactions, output, logs, plans, or screenshots that prove both the intended behavior and the control boundary.

Applied Workshop

Learners complete a controlled exercise covering OML SQL and notebook workflows for model creation and algorithm settings, sampling, class imbalance, and model metadata, then introduce a realistic exception involving experiment naming, parameter capture, repeatability, and model inventory. They record assumptions, exact steps, expected and observed results, a reversible correction, and the evidence required for operational acceptance.

1. Confirm scope, identities, data, dependencies, controls, and expected result.
2. Perform the product-specific configuration, development, analysis, or business-process task.
3. Capture reproducible technical or transaction evidence and compare it with the expected value.
4. Explain the operational, security, performance, data-quality, or business trade-off.

Validation Standard

Acceptance for Oracle Machine Learning Algorithms and Model Building requires the result to be reproducible, traceable to approved inputs, consistent with the relevant Oracle or platform behavior, and supported by checks for security, data integrity, reliability, performance, and recoverability.

• The intended target, scope, and ownership are verified.
• Expected-value and negative-path checks pass.
• Security, reliability, performance, data quality, and support impacts are reviewed.
• Unexpected behavior is resolved before acceptance.

Failure Analysis

When the result differs from expectation, learners isolate the smallest proven cause across classification, regression, clustering, anomaly detection, and association choices, OML SQL and notebook workflows for model creation, algorithm settings, sampling, class imbalance, and model metadata; they avoid speculative production changes, preserve evidence, apply the lowest-risk correction, rerun the test, and document prevention.

Practical Outcome

Complete and defend a validated result for Oracle Machine Learning Algorithms and Model Building with a reusable implementation record or runbook.