diff --git a/ai/data-science/oracle-ai-agent-memory/concepts_and_ api_walkthrough/README.md b/ai/data-science/oracle-ai-agent-memory/concepts_and_ api_walkthrough/README.md index 8490f6e13..6941d81f3 100644 --- a/ai/data-science/oracle-ai-agent-memory/concepts_and_ api_walkthrough/README.md +++ b/ai/data-science/oracle-ai-agent-memory/concepts_and_ api_walkthrough/README.md @@ -2,6 +2,10 @@ This notebook demonstrates how to use **Oracle Agent Memory** for managing and retrieving conversational memory in AI applications. +Reviewed: 2026.08.13 + +# When to use this asset? + The notebook includes: * An overview of how memory is structured and managed within Oracle Agent Memory @@ -10,13 +14,9 @@ The notebook includes: This notebook does not cover agent creation. Instead, it focuses on using Large Language Models (LLMs) to explore and demonstrate Oracle Agent Memory capabilities and API usage. -Reviewed: 2026.08.13 - -# Environment -Conda environment: python3 (ipykernel) +# How to use this asset? -# Prerequisites * Access to an Oracle Database 26ai instance with Oracle Agent Memory enabled * Installation of the oracleagentmemory Python package * Access to at least one LLM model and one embedding model from the supported model list: https://docs.oracle.com/en/database/oracle/agent-memory/26.4/agmea/get-started.html#GUID-1DC2BEC9-4CAF-4668-BBBB-E9FC57C7E71E diff --git a/ai/data-science/oracle-data-science/anomaly-detection/README.md b/ai/data-science/oracle-data-science/anomaly-detection/README.md index 8852f3a21..b1afaf8f1 100644 --- a/ai/data-science/oracle-data-science/anomaly-detection/README.md +++ b/ai/data-science/oracle-data-science/anomaly-detection/README.md @@ -11,9 +11,8 @@ The repository currently includes: Reviewed: 2026.08.13 ---- -# Projects +# When to use this asset? ## Fraud Classification @@ -26,7 +25,6 @@ The project also demonstrates production-oriented concepts such as: The deployed model can support both real-time and batch fraud monitoring workflows. The batch implementation is also covered in this project, the real time use case requires streaming tool, and not covered here. ---- ## Sales Unlabeled Anomaly Detection @@ -35,23 +33,20 @@ This project demonstrates anomaly detection for a continuous unlabeled target va The workflow includes exploratory analysis, time-series modeling and validation, anomaly detection using prediction intervals, custom model deployment, and production monitoring workflows integrated with OCI Monitoring. ---- -# Environment +# How to use this asset? +## Environment Conda environment: `generalml_p311_cpu_x86_64_v1` -Created: 2026 ---- - -# Prerequisites +## Prerequisites * Access to OCI Data Science * Required IAM permissions * Familiarity with Python and machine learning workflows ---- + # License diff --git a/ai/data-science/oracle-data-science/customizing-ads-model-deployment-artifacts/README.md b/ai/data-science/oracle-data-science/customizing-ads-model-deployment-artifacts/README.md index 64ecc65f7..d86a4c16b 100644 --- a/ai/data-science/oracle-data-science/customizing-ads-model-deployment-artifacts/README.md +++ b/ai/data-science/oracle-data-science/customizing-ads-model-deployment-artifacts/README.md @@ -16,12 +16,13 @@ Specifically, the notebook covers: 3. Customizing the generated artifact to include feature engineering in score.py 4. Registering, deploying, and invoking the model -# Environment +# How to use this asset? +## Environment Conda environment: generalml_p311_cpu_x86_64_v1 Created: April 2026 -# Prerequisites +## Prerequisites - Access to OCI Data Science - Required IAM permissions for model registration and deployment - Basic familiarity with Python, Pandas, and Scikit-learn diff --git a/ai/data-science/oracle-data-science/jobs-pipelines/README.md b/ai/data-science/oracle-data-science/jobs-pipelines/README.md index 331a412c2..4dbf2ec70 100644 --- a/ai/data-science/oracle-data-science/jobs-pipelines/README.md +++ b/ai/data-science/oracle-data-science/jobs-pipelines/README.md @@ -1,14 +1,18 @@ # Jobs & Pipelines -This package provides examples for creating and managing OCI Data Science Jobs and Pipelines using the ADS SDK. It includes three use cases: +This package provides examples for creating and managing OCI Data Science Jobs and Pipelines using the ADS SDK. +Reviewed: 2026.08.13 + +# When to use this asset? +It includes three use cases: 1. Basic Job Creation – simple job using a single script 2. Advanced Job – job using a source folder, custom variables, and flexible configuration 3. Pipeline Creation – multi-step workflows using jobs as pipeline steps -Reviewed: 2026.08.13 -# Prerequisites +# How to use this asset? +## Prerequisites To use this package, you need: @@ -16,7 +20,7 @@ To use this package, you need: - Access to the OCI Data Science Platform with appropriate Resource Principal permissions and IAM policies - An OCI Object Storage bucket -# Environment +## Environment Run the examples in a Jupyter Notebook inside an OCI Data Science Notebook Session using: generaml_p311_cpu_x86_64_v1 diff --git a/ai/data-science/oracle-data-science/model-catalog-fundamentals-and-automation/README.md b/ai/data-science/oracle-data-science/model-catalog-fundamentals-and-automation/README.md index 3602d0521..83703cbcd 100644 --- a/ai/data-science/oracle-data-science/model-catalog-fundamentals-and-automation/README.md +++ b/ai/data-science/oracle-data-science/model-catalog-fundamentals-and-automation/README.md @@ -8,39 +8,33 @@ It contains two examples with increasing levels of sophistication: Reviewed: 2026.08.13 -# Project Scope +# When to use this asset? This project covers the following topics: -1. Model Catalog Fundamentals - -* Essential Model Catalog operations, including: +Model Catalog Fundamentals 1. Model serialization 2. Model registration in the Model Catalog 3. Loading a registered model and using it for inference 4. Retrieving model metadata from the catalog (using multiple approaches) 5. Updating a registered model’s metadata (using multiple approaches) -* The implementation heavily relies on the ADS SDK. -* Note: Model deployment is out of scope for this notebook. - -2. Automated Model Registration - -* Automating the model registration process using OCI Data Science Jobs and model version sets, including: +Automated Model Registration 1. Defining a model version set 2. Adding multiple model versions to a version set via job runs 3. Implementing a retention mechanism for archiving older model versions 4. Use of Object stroage in the JupyterLab and Jobs -* This example also heavily relies on the ADS SDK. -# Prerequisites + +# How to use this asset? +## Prerequisites * Access to the OCI Data Science Platform * Basic familiarity with Python and machine learning concepts * An OCI compartment with: 1. Resource principal configured 2. Appropriate IAM policies for OCI Data Science, Object Storage, and Model Catalog -# How to Use +## How to Use 1. Open the notebooks in an OCI Data Science Notebook Session. 2. Select the following conda environment: generalml_p311_cpu_x86_64_v1 3. Run the notebook cells sequentially to reproduce each workflow: diff --git a/ai/data-science/oracle-data-science/operational-research/README.md b/ai/data-science/oracle-data-science/operational-research/README.md index ddf17c150..981252682 100644 --- a/ai/data-science/oracle-data-science/operational-research/README.md +++ b/ai/data-science/oracle-data-science/operational-research/README.md @@ -8,7 +8,7 @@ This project presents three operational research use cases with different levels Reviewed: 2026.08.13 -# What You’ll Learn +# When to use this asset? This project covers the following topics: @@ -22,11 +22,6 @@ This project covers the following topics: - Endpoint invocation for predictions - Data Science Jobs -# Prerequisites - -- Access to the OCI Data Science Platform -- Basic familiarity with Python and machine learning concepts -- A valid OCI compartment, resource principal, and policies configured for Data Science services # How to Use this asset? @@ -34,6 +29,12 @@ This project covers the following topics: 2. Select the following conda environment: generalml_p311_cpu_x86_64_v1 3. Run the notebook cells in sequence to reproduce the complete workflow. +## Prerequisites + +- Access to the OCI Data Science Platform +- Basic familiarity with Python and machine learning concepts +- A valid OCI compartment, resource principal, and policies configured for Data Science services + # License Copyright (c) 2026 Oracle and/or its affiliates. diff --git a/ai/data-science/oracle-data-science/registration-and-deployment-with-oci-sdk/README.md b/ai/data-science/oracle-data-science/registration-and-deployment-with-oci-sdk/README.md index f097e9a7c..c25b0f214 100644 --- a/ai/data-science/oracle-data-science/registration-and-deployment-with-oci-sdk/README.md +++ b/ai/data-science/oracle-data-science/registration-and-deployment-with-oci-sdk/README.md @@ -1,18 +1,22 @@ # Registration and Deployment with OCI SDK This project demonstrates how to register and deploy a machine learning model using the OCI SDK, while using the ADS SDK to create the model artifact. -Although ADS simplifies model registration and deployment, it can be limiting for advanced scenarios. In this notebook, we use the OCI SDK to: +Although ADS simplifies model registration and deployment, it can be limiting for advanced scenarios. + +Reviewed: 2026.08.13 + +# When to use this asset? +In this notebook, we use the OCI SDK to: - Register a model in a different compartment than the Notebook Session - Deploy the model to that target compartment - Move an existing model deployment between compartments -Reviewed: 2026.08.13 - -# Environment +# How to use this asset? +## Environment Conda environment: automlx251_p311_cpu_x86_64_v2 -# Prerequisites +## Prerequisites - Access to OCI Data Science - Required IAM permissions for model registration and deployment - Basic familiarity with Python and OCI SDK diff --git a/ai/data-science/oracle-data-science/your-first-data-science-project/README.md b/ai/data-science/oracle-data-science/your-first-data-science-project/README.md index 46e9d4ff4..1630eb750 100644 --- a/ai/data-science/oracle-data-science/your-first-data-science-project/README.md +++ b/ai/data-science/oracle-data-science/your-first-data-science-project/README.md @@ -6,37 +6,28 @@ The project walks through the main stages of a typical machine learning lifecycl Reviewed: 2026.08.13 -# What You’ll Learn +# When to use this asset? -This project covers the following steps: +1. This project covers the following steps: +2. Data ingestion +3. Data preprocessing and visualization +4. Model training and validation +5. Model explainability +6. Model deployment +7. Endpoint invocation for predictions -Data ingestion +# How to use this asset? +## Prerequisites -Data preprocessing and visualization +* Access to OCI Data Science Platform +* Basic familiarity with Python and machine learning concepts +* A valid compartment, resource principal and policies configured for Data Science services. More details can be found in the Guide for Your First Data Science Project prerequisites.pdf -Model training and validation +## How to Use -Model explainability - -Model deployment - -Endpoint invocation for predictions - -# Prerequisites - -Access to OCI Data Science Platform - -Basic familiarity with Python and machine learning concepts - -A valid compartment, resource principal and policies configured for Data Science services. More details can be found in the Guide for Your First Data Science Project prerequisites.pdf - -# How to Use - -Open the provided notebook in your OCI Data Science Notebook Session. - -Select the following conda environment: automlx234_p310_cpu_x86_64_v1 - -Run the notebook cells in sequence to reproduce the complete workflow. +* Open the provided notebook in your OCI Data Science Notebook Session. +* Select the following conda environment: automlx234_p310_cpu_x86_64_v1 +* Run the notebook cells in sequence to reproduce the complete workflow. # License diff --git a/ai/data-science/oracle-vector-search/python-rag-wikipedia-search/README.md b/ai/data-science/oracle-vector-search/python-rag-wikipedia-search/README.md index cc2298d06..670c22408 100644 --- a/ai/data-science/oracle-vector-search/python-rag-wikipedia-search/README.md +++ b/ai/data-science/oracle-vector-search/python-rag-wikipedia-search/README.md @@ -12,7 +12,8 @@ Use this asset as support material when wanting to showcase Oracle AI Vector Sea 3. Running hybrid search (keyword + vector similarity) 4. Applying rerankers to improve retrieved context -# Prerequisites +# How to use this asset? +## Prerequisites To use this package, you need: - Basic Python knowledge @@ -21,7 +22,7 @@ To use this package, you need: - Required IAM permissions for Data Science and Database access - A configured database wallet or secure connection details -# Environment +## Environment Run the examples in a Jupyter Notebook inside an OCI Data Science Notebook Session. diff --git a/ai/data-science/oracle-vector-search/sql-your-first-vector-project/README.md b/ai/data-science/oracle-vector-search/sql-your-first-vector-project/README.md index ee3770858..03d009206 100644 --- a/ai/data-science/oracle-vector-search/sql-your-first-vector-project/README.md +++ b/ai/data-science/oracle-vector-search/sql-your-first-vector-project/README.md @@ -12,8 +12,6 @@ Use the scripts to learn basics about AI Vector Search. Please use the posting [Getting started with vectors in 26ai](https://blogs.oracle.com/coretec/post/getting-started-with-vectors-in-23ai) for information about the scripts. -# Useful Links - ## Documentation - [Oracle AI Vector Search User's Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/overview-ai-vector-search.html)