Develop essential systems & infrastructure skills with expert instruction and practical examples.
This course is structured to help learners prepare for an MCQ-based assessment focused on Oracle Cloud Infrastructure (OCI) Data Science capabilities and use cases. 1. OCI Data Science FundamentalsOCI Data Science overview and architectureCore components: Workspaces, Projects, Notebooks, ModelsOCI tenancy and compartment configuration2.
Accelerated Data Science (ADS) SDKKey features: data profiling, AutoML, model training, and explanationsADS SDK installation and usage scenarios3. Workspace and Environment SetupCreating and managing projects and notebook sessionsConda environments: setup and customizationUsing OCI Vault for secure credential storageGit integration with Code Repositories4. Machine Learning LifecycleML lifecycle stages: data ingestion, preparation, modeling, deploymentFetching data from OCI and external sourcesVisualization and profiling using ADSModel training with open-source and Oracle AutoMLEvaluation metrics and model explanation techniquesRegistering, deploying, and invoking models via Model Catalog5.
Advanced AI IntegrationUsing ADS with OCI Generative AI servicesLangChain application deployment on OCI6. MLOps and AutomationOCI MLOps architectureCreating and managing jobsAutoscaling model deployments for inferenceLogging, monitoring, and pipeline automation7. Related OCI ServicesRunning Spark apps via OCI Data FlowOCI Open Data Service overviewDataset creation with OCI Data Labeling service This course and its content are designed strictly for educational and exam preparation purposes only.
The material presented is not affiliated with or endorsed by Oracle Corporation.
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