Using Oracle Machine Learning with Autonomous Database 2024 ELS

Course Description

Using Oracle Machine Learning with Autonomous Database 2024 ELS develops practical capability in autonomous database, oml architecture, workspaces, data access, and security and data exploration, preparation, feature engineering, and sql-based analytics. The course connects Oracle architecture, configuration, development, or functional processing to ownership, security, data quality, controls, and measurable acceptance criteria.

The later modules cover classification, regression, clustering, anomaly detection, and model evaluation and model deployment, scoring, explainability, monitoring, and lifecycle governance. Learners use workspace configuration, data profiles, feature transformations, model metrics to distinguish configuration defects from data, access, integration, performance, or operational failures.

By completing the learning path, participants can support an analytics team building and operationalizing an in-database machine-learning model without exporting governed enterprise data. They also produce an improvement plan covering validation, exception handling, rollback, ownership, monitoring, and production support.

What You Will Learn

• Interpret the architecture and decision points associated with Autonomous Database, OML Architecture.
• Prepare Workspaces, Data Access, Security for production use and confirm it through workspace configuration.
• Perform Data Exploration, Preparation with explicit inputs, outputs, and exception paths.
• Detect data leakage early by monitoring Feature Engineering, SQL-Based Analytics for abnormal behaviour.
• Integrate Classification, Regression into the wider workflow instead of treating it as an isolated task.
• Reconcile Clustering, Anomaly Detection, Model Evaluation with scoring results and assign ownership for unresolved differences.
• Optimise Model Deployment, Scoring around reliability, security, performance, and user outcomes.
• Create a support playbook for unauthorized data access that uses Explainability, Monitoring, Lifecycle Governance to guide diagnosis and rollback.

SkillDynamo Training Approach

Delivery combines Oracle-specific demonstrations, guided hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, peer review, and a final improvement plan. Learners document assumptions, settings, data, outputs, exceptions, corrective actions, rollback needs, approvals, evidence, and operational responsibilities.