Oracle Cloud Infrastructure Data Science Professional (2024): Hands-On Workshop — Lesson 1: OCI Data Science Projects, Notebook Sessions, Environments, Storage, IAM, and Reproducibility

Learning Objectives

• Trace prerequisites, dependencies, ownership, and failure conditions for oci data science projects, notebook sessions, environments, storage, iam, and reproducibility.
• Configure the course-specific workflow while collecting evidence at each material checkpoint.
• Defend the solution against security, compliance, reliability, performance, and support expectations.

Detailed Topics

• OCI Data Science Projects
• Notebook Sessions
• Environments
• Storage
• IAM
• Reproducibility
• requirements, ownership, prerequisites, data, and acceptance criteria for oci data science projects, notebook sessions, environments, storage, iam, and reproducibility
• security, controls, integration, performance, recovery, and support implications for oci data science projects, notebook sessions, environments, storage, iam, and reproducibility

Instructor Demonstration

Demonstrate oci data science projects, notebook sessions, environments, storage, iam, and reproducibility within a governed machine-learning solution built in OCI Data Science from reproducible data preparation through evaluated deployment and monitored inference. Use representative data and capture project configuration, notebook environments, data profiles, feature transformations, experiment runs, evaluation metrics, explanation output, model metadata, endpoint tests, and monitoring logs so another practitioner can independently verify the result.

Hands-On Exercise

Complete a controlled Oracle Cloud Infrastructure Data Science Professional (2024): Hands-On Workshop exercise focused on oci data science projects, notebook sessions, environments, storage, iam, and reproducibility. Introduce one realistic failure involving unreproducible environments, data leakage, poor feature quality, unsuitable metrics, biased models, missing metadata, insecure endpoints, drift, and failed retraining; isolate the cause, apply the smallest reversible correction, and rerun the acceptance checks.

1. Record the requirement, environment, identities, dependencies, data, controls, and expected result.
2. Perform the lesson workflow and capture evidence before and after each material change.
3. Test normal, boundary, failure, security, reconciliation, recovery, and operational conditions relevant to the topic.
4. Document the root cause, correction, rollback, owner, support action, and next improvement priority.

Scenario Analysis

• Establish the current state, desired state, constraints, and tolerance for disruption.
• Compare alternate implementation or diagnostic approaches and their rollback implications.
• Document the decision, evidence, exceptions, follow-up actions, and measurable improvement target.

Troubleshooting Guidance

When oci data science projects, notebook sessions, environments, storage, iam, and reproducibility does not produce the expected result, confirm scope, effective dates, identities, data context, dependencies, and recent changes. Review the Oracle-specific logs, reports, traces, calculations, workflow history, transaction status, or diagnostic output relevant to the lesson. Test one reversible hypothesis at a time and verify that the correction does not create unreproducible environments, data leakage, poor feature quality, unsuitable metrics, biased models, missing metadata, insecure endpoints, drift, and failed retraining elsewhere in the process.

Validation Checklist

• Prerequisites, ownership, data context, settings, and expected outcomes are documented.
• The workflow is reproducible and supported by project configuration, notebook environments, data profiles, feature transformations, experiment runs, evaluation metrics, explanation output, model metadata, endpoint tests, and monitoring logs.
• Security, controls, data quality, integration, performance, recovery, and support conditions are reviewed.
• Exceptions, corrective action, rollback, approvals, monitoring, and handover are recorded.

Lesson Outcome

Learners can apply OCI Data Science Projects, Notebook Sessions, Environments, Storage, IAM, and Reproducibility in a realistic Oracle Cloud Infrastructure Data Science Professional (2024): Hands-On Workshop situation and defend the result with course-specific evidence.