oracle-samples/oci-data-science-ai-samples

This repo contains a series of tutorials and code examples highlighting different features of the OCI Data Science and AI services, along with a release vehicle for experimental programs.

What it solves

Oracle Cloud Infrastructure (OCI) Data Science and AI services are powerful but have many features that can be hard to learn. This repository collects demos, tutorials, and code examples that show how to use those services in practice. It helps data scientists and developers get started faster, avoid common pitfalls, and apply features like model deployment, jobs, pipelines, and LLM support without having to figure out everything from scratch.

How it works

The project is organized into folders, each covering a specific area of OCI Data Science or AI services. Most examples are Jupyter notebooks or Python/Java scripts that walk through a task step by step. For example:

  • Notebook examples show how to use the Accelerated Data Science (ADS) SDK to speed up common data science tasks, like storing secrets in OCI Vault.
  • Conda environment notebooks demonstrate how to use pre-built conda environments for common data science tasks.
  • Labs provide end-to-end examples of training and deploying machine learning models.
  • The LLM section shows ways to fine-tune, deploy, and evaluate large language models, including no-code options via AI Quick Actions and code-based approaches with LangChain.
  • Model catalog examples explain how to create score.py and runtime.yaml files needed for deployment.
  • Compute targets, capacity reservation discovery, jobs, distributed training, pipelines, ML applications, data labeling, notebook lifecycle scripts, and feature store each have their own dedicated examples and documentation.

All examples are meant to be run on OCI Data Science and are designed to be practical and reusable.

Who it’s for

This repository is for data scientists, machine learning engineers, and developers who use (or plan to use) Oracle Cloud Infrastructure Data Science and AI services. It is useful for beginners who need guided tutorials and for experienced practitioners who want concrete examples of advanced features like distributed training, pipelines, and ML applications. The content is also useful for anyone looking to automate machine learning workflows or deploy models in production on OCI.

Highlights

  • Broad coverage: Examples span notebooks, conda environments, model deployment, jobs, distributed training, pipelines, ML applications, data labeling, feature store, and more.
  • End-to-end tutorials: Labs walk through training and deploying models on the OCI Data Science service.
  • LLM support: Includes no-code AI Quick Actions, LangChain integration, and direct Python coding examples.
  • Reusable scripts: Lifecycle scripts and data labeling scripts are ready to use with minimal editing.
  • Actively maintained: The project welcomes community contributions and encourages users to open issues for feedback or missing content.

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