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For the top 15 GitHub repos, ask for 10 paragraphs. e.g. Amazon SageMaker Features, Amazon SageMaker Alternatives, Amazon SageMaker Security, , Amazon SageMaker DevOps
Introduced in 2023, Oracle's Guardian AI is an open-source library designed to assess the fairness, bias, and privacy of machine learning models and datasets. It offers tools to diagnose unintended biases and estimate potential leakage of sensitive information through membership inference attacks, promoting responsible AI development.
The OCI Data Science AI Samples repository provides a collection of practical examples and notebooks for Oracle Cloud Infrastructure (OCI) Data Science services. These resources cover various topics, including data preparation, model training, hyperparameter optimization, and batch inference, aiding data scientists in effectively utilizing OCI's capabilities.
https://github.com/oracle-samples/oci-data-science-ai-samples
Developed by Oracle Labs, AutoMLx is a package that simplifies the process of training and explaining machine learning models. The repository contains demo notebooks demonstrating how to initialize, train, and interpret models using AutoMLx, highlighting its advanced features for automated machine learning and explainability.
The Oracle AI Microservices Sandbox offers a streamlined environment for developers and data scientists to explore generative artificial intelligence combined with retrieval-augmented generation (RAG) capabilities. By integrating Oracle Database 23c AI Vector Search, it enhances existing large language models through RAG, facilitating advanced AI explorations.
Oracle Machine Learning (OML) is a suite of tools that enable scalable, automated, and secure data science and machine learning within the Oracle Database and Oracle Autonomous Database. It supports multiple languages, including SQL, R, and Python, allowing users to build, evaluate, deploy, and monitor models directly within the database environment.
https://github.com/oracle-samples/oracle-db-examples/blob/main/machine-learning/README.md
The OCI MLflow plugin empowers users by providing seamless integration between MLflow and Oracle Cloud Infrastructure resources. This integration facilitates effective management of the entire machine learning lifecycle, including experiment tracking, model deployment, and scalability within the OCI ecosystem.
OML4Py enables AI and machine learning capabilities within the Oracle Database using the popular Python programming language. This workshop-focused repository provides hands-on activities, guiding users through data acquisition, feature engineering, model creation, and optimization using OML4Py and Jupyter notebooks.
This repository offers practical examples demonstrating how Oracle Machine Learning enables teams to collaborate in building, assessing, and deploying machine learning models. It focuses on simplifying the machine learning process for data science, from preparation through deployment, within the Autonomous Data Warehouse Cloud (ADWC).
Tribuo, developed by Oracle, is a machine learning library in Java that provides functionalities for multi-class classification, regression, clustering, anomaly detection, and multi-label classification. It includes implementations of popular machine learning algorithms and wraps other libraries to offer a unified interface for data loading, feature extraction, and transformation.
Oracle Machine Learning offers a range of features, including support for multiple languages (SQL, R, Python), AutoML capabilities, no-code user interfaces, and in-database machine learning. These features enable users to develop scalable machine learning solutions, perform automated model tuning and selection, and deploy models seamlessly within the Oracle Database environment.
https://www.oracle.com/artificial-intelligence/database-machine-learning/features/
The Oracle Accelerated Data Science (ADS) SDK, introduced in 2020, is designed to expedite common data science activities by automating and simplifying tasks such as data acquisition, model evaluation, and deployment. It provides a Pythonic interface to various Oracle Cloud Infrastructure (OCI) services, including Data Science, Model Catalog, and Object Storage, enhancing productivity for data scientists working within the OCI ecosystem.
Oracle Machine Learning for Python (OML4Py) enables AI and machine learning capabilities within the Oracle Database using the popular Python programming language. This workshop-focused repository provides hands-on activities, guiding users through data acquisition, feature engineering, model creation, and optimization using OML4Py and Jupyter notebooks.
Oracle Machine Learning offers a range of features, including support for multiple languages (SQL, R, Python), AutoML capabilities, no-code user interfaces, and in-database machine learning. These features enable users to develop scalable machine learning solutions, perform automated model tuning and selection, and deploy models seamlessly within the Oracle Database environment.
https://www.oracle.com/artificial-intelligence/database-machine-learning/features/
This repository offers practical examples demonstrating how Oracle Machine Learning enables teams to collaborate in building, assessing, and deploying machine learning models. It focuses on simplifying the machine learning process for data science, from preparation through deployment, within the Autonomous Data Warehouse Cloud (ADWC).
Tribuo, developed by Oracle, is a machine learning library in Java that provides functionalities for multi-class classification, regression, clustering, anomaly detection, and multi-label classification. It includes implementations of popular machine learning algorithms and wraps other libraries to offer a unified interface for data loading, feature extraction, and transformation.
Oracle Machine Learning (OML) is a suite of tools that enable scalable, automated, and secure data science and machine learning within the Oracle Database and Oracle Autonomous Database. It supports multiple languages, including SQL, R, and Python, allowing users to build, evaluate, deploy, and monitor models directly within the database environment.
https://github.com/oracle-samples/oracle-db-examples/blob/main/machine-learning/README.md
The OCI Data Science AI Samples repository provides a collection of practical examples and notebooks for Oracle Cloud Infrastructure (OCI) Data Science services. These resources cover various topics, including data preparation, model training, hyperparameter optimization, and batch inference, aiding data scientists in effectively utilizing OCI's capabilities.
https://github.com/oracle-samples/oci-data-science-ai-samples
Developed by Oracle Labs, AutoMLx is a package that simplifies the process of training and explaining machine learning models. The repository contains demo notebooks demonstrating how to initialize, train, and interpret models using AutoMLx, highlighting its advanced features for automated machine learning and explainability.
The Oracle AI Microservices Sandbox offers a streamlined environment for developers and data scientists to explore generative artificial intelligence combined with retrieval-augmented generation (RAG) capabilities. By integrating Oracle Database 23c AI Vector Search, it enhances existing large language models through RAG, facilitating advanced AI explorations.
Introduced in 2023, Oracle's Guardian AI is an open-source library designed to assess the fairness, bias, and privacy of machine learning models and datasets. It offers tools to diagnose unintended biases and estimate potential leakage of sensitive information through membership inference attacks, promoting responsible AI development.