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2 changes: 1 addition & 1 deletion data-platform/data-science/README.md
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Expand Up @@ -8,7 +8,7 @@ The Data Science section covers the tools and services under the broader Oracle
* Oracle Graph (In-Database)
* Oracle Spatial (In-Databse)

Reviewed: 2025.04.19
Reviewed: 2025.09.26


# License
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2 changes: 1 addition & 1 deletion data-platform/data-science/oracle-data-science/README.md
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Expand Up @@ -4,7 +4,7 @@ This page covers Oracle Cloud Infrastructure (OCI) Data Science Service.

Oracle Cloud Infrastructure (OCI) Data Science is a fully-managed platform for teams of data scientists to build, train, deploy, and manage machine learning models using Python and open source tools. Use a JupyterLab-based environment to experiment and develop models. Scale up model training with NVIDIA GPUs and distributed training. Take models into production and keep them healthy with MLOps capabilities, such as automated pipelines, model deployments, and model monitoring.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# Table of Contents
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This document covers how to use AI Quick Actions within OCI Data Science to deploy an out of the box foundational model from the AI Quick Actions Catalog.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers how to use AI Quick Actions within OCI Data Science to fine tune a foundational model on a domain-specific dataset.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers how to use AI Quick Actions within OCI Data Science to evaluate an already deployed foundational model that was deployed via AI Quick Actions.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers how to use AI Quick Actions within OCI Data Science to deploy a verified foundational model downloaded from Hugging Face.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This files cover code snippets for OCI Data Science, for enhancing data with GAN (Generative Adversarial Networks).

Reviewed: 2025.03.02
Reviewed: 2025.09.26


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This files cover code snippets for OCI Data Science, for generating synthetic data.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


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This document covers the topic of GitHub integration for OCI Data Science.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This files cover code snippets to get started with OCI Data Science jobs utilising the ADS library.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


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This files cover code snippets and documentation for utilising OCI Data Science Jobs for Distributed Training.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers the topic of submitting local code as OCI Data Science Jobs using the ADS Operator Control.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers how to integrate NVIDIA NIM Microservices within OCI Data Science by deploying the Mistral 7B Instruct v0.3 LLM via the OCI Marketplace.

Reviewed: 2025.04.19
Reviewed: 2025.09.26


# When to use this asset?
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This document contains code assets and doucmentation for setting up and integrating OCI Data Science with MLflow Tracking Server on OCI.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers provisioning of the AI All-in-One Data Science Image from the OCI Markplace.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document contains the code and documentation on how to install an R Conda environment within OCI Data Science.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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2 changes: 1 addition & 1 deletion data-platform/data-science/oracle-graph/README.md
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Expand Up @@ -4,7 +4,7 @@ This page covers Oracle Graph.

Graph databases, part of Oracle’s converged database offering, eliminate the need to set up a separate database and move data. Analysts and developers can perform fraud detection in banking, find connections and links to data, and improve traceability in smart manufacturing, all while gaining enterprise-grade security, ease of data ingestion, and strong support for data workloads.

Reviewed: 2025.04.19
Reviewed: 2025.09.26


# Useful Links
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This file covers code snippets for Oracle Property Graph, for the Explore Graph Model step.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This file covers code snippets for Oracle Property Graph, for the Extract Graph Metrics step.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This file covers code snippets for Oracle Machine Learning, for the Prepare Data for Graph step.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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Expand Up @@ -4,7 +4,7 @@ This page covers Oracle Machine Learning, or Machine Learning in Oracle Database

Machine Learning in Oracle Database supports data exploration, preparation, and machine learning modeling at scale using SQL, R, Python, REST, automated machine learning (AutoML), and no-code interfaces. It includes more than 30 high-performance in-database algorithms producing models for immediate use in applications. By keeping data in the database, organizations can simplify their overall architecture and maintain data synchronization and security. It enables data scientists and other data professionals to build models quickly by simplifying and automating key elements of the machine learning lifecycle.

Reviewed: 2025.03.02
Reviewed: 2025.09.26

# Team Publications

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This file covers code snippets for Oracle Machine Learning, for the Create Machine Learning Models step.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This file covers code snippets for Oracle Machine Learning, for the Explore Enhanced Data step.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers installation of OML4R on Oracle Database.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This file covers code snippets for Oracle Machine Learning, for the Prepare Data for Machine Learning step.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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2 changes: 1 addition & 1 deletion data-platform/data-science/oracle-select-ai/README.md
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This page covers Oracle Select AI.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# License
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This file covers OML code snippets for utilising the Select AI Natural Language to SQL capabilities. Please run the select-ai-intro-setup content first located in the GitHub directory above.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This file covers OML code snippets for utilising the Select AI Retrieval Augment Generation capabilities. Please run the select-ai-intro-setup content first located in the GitHub directory above.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This file covers OML code snippets for all the pre-requisites that are required to be executed before utilisng the Select AI capabilities.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This file covers OML code snippets for utilising the Select AI Synthetic Generation capabilities. Please run the select-ai-intro-setup content first located in the GitHub directory above.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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2 changes: 1 addition & 1 deletion data-platform/data-science/oracle-spatial/README.md
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Expand Up @@ -4,7 +4,7 @@ This page covers Oracle Spatial.

Oracle’s spatial database is an integrated part of Oracle's converged database, enabling developers, analysts, and geographic information system (GIS) professionals to manage geospatial data, perform analysis, add location-based services to applications, and visualize through interactive maps.

Reviewed: 2025.04.19
Reviewed: 2025.09.26


# Useful Links
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2 changes: 1 addition & 1 deletion data-platform/data-science/oracle-vector-search/README.md
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This page covers Oracle Vector Search.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# License
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This document covers how to configure the in-memory vector index size within Oracle DB 23ai.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers the topic of different methods to allow you to connect to Oracle DB 23ai.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers the topic of loading documents to an Oracle DB 23ai (Non-Autonomous) table.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers the provisioning of Oracle Base Database Service 23ai.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers the topic of creating embeddings for Vector Search using OCI GenAI Embedding Model.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers the topic of creating embeddings for Vector Search using the Sentence Transformer Embedding Model.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This document covers the insertion of a vector into a simple table containing the vector data type.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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2 changes: 1 addition & 1 deletion data-platform/data-science/shared-assets/README.md
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This page covers shared assets, meaning assets that are not single product specific, and that cover either multiple products or a broader scope.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# License
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This page covers the Art of Possible Demo-associated assets.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# License
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This demo is going to showcase predicting the creditworthiness of a loan applicant.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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The objective is not only to make accurate predictions but also to understand the reasons why our Machine Learning Model has predicted each patient to be a show or a no-show.
By exploring the explainability of a model we will be able to answer the question of ‘why’ a patient will likely be a no-show so that the practice operational team can follow up with a friendly reminder or reschedule for a more convenient time.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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This demo is going to show Oracle AI Platform capabilities to predict the number of customers visiting a Retail space by following a composite AI approach.
The objective is to make accurate predictions about future footfall and make data-driven decisions about staffing, inventory, and rental prices as well as optimize tenant mix by combining various techniques such as descriptive analytics, NLP, image detection, and forecasting.

Reviewed: 2025.03.02
Reviewed: 2025.09.26


# When to use this asset?
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