Machine Learning on Oracle — Lesson 1: Oracle Machine Learning Concepts, Data Sources, Preparation, and Sampling

Learning Objectives

• Explain the purpose and operating context of in-database machine learning architecture, use cases, and data roles.
• Apply case identifiers, targets, attributes, datatypes, and analytical views and missing values, outliers, normalization, binning, and categorical preparation in a controlled environment.
• Validate outcomes and troubleshoot risks involving training, testing, time splits, leakage prevention, and preparation evidence.

Detailed Topics

• in-database machine learning architecture, use cases, and data roles
• case identifiers, targets, attributes, datatypes, and analytical views
• missing values, outliers, normalization, binning, and categorical preparation
• training, testing, time splits, leakage prevention, and preparation evidence

Instructor Demonstration

Demonstrate in-database machine learning architecture, use cases, and data roles for a customer-risk use case modeled inside Oracle Database from source data to governed scoring; 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 Machine Learning on Oracle exercise covering case identifiers, targets, attributes, datatypes, and analytical views and missing values, outliers, normalization, binning, and categorical preparation; introduce an exception involving training, testing, time splits, leakage prevention, and preparation evidence, 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 in-database machine learning architecture, use cases, and data roles, case identifiers, targets, attributes, datatypes, and analytical views, missing values, outliers, normalization, binning, and categorical preparation, training, testing, time splits, leakage prevention, and preparation evidence 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 Oracle Machine Learning Concepts, Data Sources, Preparation, and Sampling in a realistic Machine Learning on Oracle scenario and defend the result with documented evidence.