Oracle Cloud Infrastructure Data Science Professional

Course Description

Oracle Cloud Infrastructure Data Science Professional develops practical capability in designing professional OCI Data Science architectures with workspaces, data access, training, deployment, pipelines, monitoring, governance, and cost controls. The learning path is organized around an enterprise model portfolio designed for secure collaboration, repeatable delivery, and monitored inference.

Learners progress from product architecture and workflow understanding into configuration, development, analysis, validation, troubleshooting, and operational handoff. Every lesson uses the controls, tools, data structures, risks, failure modes, and decisions relevant to this exact Oracle pathway.

The content is independently written for SkillDynamo using the supplied source page for title, update date, and topic alignment. Learners finish with reproducible evidence, a course-specific implementation record or runbook, and a prioritized improvement action.

What You Will Learn

• Explain and apply OCI Data Science service components, project organization, and tenancy design within Oracle Cloud Infrastructure Data Science Professional.
• Configure, validate, or troubleshoot private networking, service gateways, Object Storage, and data access, dynamic groups, policies, resource principals, Vault, and collaboration, and shapes, block storage, conda, Git, quotas, cost, and workspace standards using course-specific evidence.
• Explain and apply database, Object Storage, streaming awareness, and dataset connectivity within Oracle Cloud Infrastructure Data Science Professional.
• Configure, validate, or troubleshoot profiling, data quality, leakage, sampling, splits, and transformations, ADS datasets, feature engineering, reproducibility, and lineage, and experiments, metadata, version control, environment capture, and evidence using course-specific evidence.
• Explain and apply algorithm selection, training strategies, hyperparameters, and distributed awareness within Oracle Cloud Infrastructure Data Science Professional.
• Configure, validate, or troubleshoot AutoML, tuning, model comparison, and experiment ranking, metrics, imbalance, thresholds, uncertainty, and error analysis, and explainability, fairness, model catalog metadata, approvals, and risk using course-specific evidence.
• Explain and apply model artifacts, runtime environments, deployments, endpoints, and scaling within Oracle Cloud Infrastructure Data Science Professional.
• Configure, validate, or troubleshoot jobs, pipelines, schedules, retraining, and CI/CD integration awareness, latency, throughput, logs, alarms, data drift, model drift, and incidents, and security, audit, retention, cost, ownership, retirement, and MLOps runbook using course-specific evidence.

SkillDynamo Training Approach

The training follows an enterprise model portfolio designed for secure collaboration, repeatable delivery, and monitored inference. Activities combine product-specific demonstrations, guided implementation, hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, solution review, and a final improvement plan. Learners document assumptions, settings, commands or transactions, outputs, exceptions, corrective actions, security and governance trade-offs, rollback needs, and operational ownership.