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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 2018, the AI Fairness 360 Toolkit is an open-source library designed to help developers detect and mitigate bias in machine learning models. It offers over 70 fairness metrics and 10 state-of-the-art bias mitigation algorithms, facilitating the creation of fairer AI systems.
Launched in 2018, the Adversarial Robustness Toolbox (ART) is an open-source library dedicated to machine learning security. It provides tools to assess and enhance the robustness of AI models against adversarial attacks, supporting the development of secure and reliable AI systems.
In 2020, IBM introduced a library supporting federated learning, enabling decentralized training of machine learning models across multiple devices while preserving data privacy. This approach facilitates collaborative model development without centralized data collection.
Launched in 2018, the Model Asset Exchange (MAX) is an open-source platform offering a curated collection of machine learning models ready for deployment. It provides pre-trained models across various domains, simplifying the integration of AI capabilities into applications.
Released in 2023, the AI Privacy Toolkit is a collection of tools and techniques focused on the privacy and compliance of AI models. It includes methods for anonymizing machine learning model training data, ensuring that retrained models maintain data privacy standards.
Introduced in 2021, AIMMX is a tool that automatically extracts and infers AI model-related metadata from software repositories. It aids in organizing and managing model information, enhancing transparency and reproducibility in AI research.
The EdgeAI APIs for IBM Developer repository provides a set of RESTful web services with data and AI algorithms to support AI applications across distributed hybrid cloud and edge environments. These APIs enable efficient data analysis at the source, reducing the need to move vast amounts of data.
The AI Fairness 360 Toolkit Explained repository offers comprehensive explanations and tutorials on using the AI Fairness 360 Toolkit. It serves as a valuable resource for understanding and implementing fairness metrics and bias mitigation algorithms in machine learning models.
The AI Hardware Acceleration Kit (AIHWKit) is a Python library that simulates inference and training of deep neural networks using analog in-memory computing hardware. It provides tools for adapting DNNs to achieve equivalent accuracy on hardware with noisy and non-linear device characteristics.
In May 2024, IBM open-sourced its Granite Code Models, a series of AI foundation models tailored for enterprise software development. These models, available under the Apache 2.0 license, are designed to enhance coding efficiency and support various programming tasks.
https://github.com/ibm-granite/granite-code-models
In 2019, IBM introduced the AI Explainability 360 (AIX360) toolkit, an open-source library designed to support the interpretability and explainability of machine learning models. AIX360 provides a comprehensive suite of algorithms and metrics to help developers and data scientists understand and interpret the behavior of AI systems, promoting transparency and trustworthiness in AI applications.
Launched in 2019, IBM's Speech by Crowd is an AI system that aggregates diverse viewpoints from large audiences on controversial topics. By utilizing natural language processing and understanding techniques, it synthesizes collected arguments into coherent narratives, aiding in informed decision-making and public discourse.
Introduced in 2019, NeuNetS is a tool that automates the design of neural network architectures. It enables users to generate optimized deep learning models tailored to specific datasets and tasks without requiring extensive expertise in neural network design, streamlining the development process for AI solutions.
In 2020, IBM released a Federated Learning Framework that facilitates the training of machine learning models across decentralized data sources while preserving data privacy. This approach allows multiple parties to collaboratively build models without sharing sensitive data, enhancing privacy and security in AI applications.
Launched in 2018, AI OpenScale is a platform that provides insights into AI models' operations, ensuring they are fair, explainable, and compliant. It offers tools for monitoring and managing AI models in production, helping organizations maintain transparency and trust in their AI systems.
Introduced in 2018, the Model Asset eXchange (MAX) is an open-source platform that hosts a curated collection of machine learning models ready for use in various applications. MAX provides developers with access to pre-trained models, facilitating the integration of AI capabilities into their projects.
Released in 2021, the AI Privacy Toolkit is a collection of tools designed to help developers address privacy concerns in AI systems. It includes methods for data anonymization and techniques to ensure compliance with privacy regulations, supporting the development of responsible AI applications.
In 2019, IBM introduced the AI Explainability 360 Toolkit, an open-source library aimed at improving the interpretability of machine learning models. It offers a range of algorithms and metrics to help users understand AI model decisions, promoting transparency in AI systems.
Launched in 2018, the AI Fairness 360 Toolkit is an open-source library that provides metrics and algorithms to detect and mitigate bias in machine learning models. It assists developers in creating fairer AI systems by addressing issues related to bias and discrimination.
Introduced in 2018, the Adversarial Robustness Toolbox is an open-source library dedicated to machine learning security. It offers tools to assess and improve the robustness of AI models against adversarial attacks, ensuring the reliability of AI applications.
https://github.com/IBM/adversarial-robustness-toolbox
The [IBM Watson Machine Learning Samples](https://github.com/IBM/watson-machine-learning-samples) repository offers a collection of notebooks that demonstrate the capabilities of Watson Machine Learning and watsonx.ai. These samples cover various aspects, including AutoAI, deep learning, and the deployment of models such as scikit-learn and XGBoost. They serve as practical guides for users of IBM's AI platforms, providing hands-on examples to facilitate understanding and implementation.
The [IBM PowerAI](https://github.com/IBM/powerai) repository contains ancillary and supplemental information to assist users of Watson Machine Learning Community Edition (WML CE). It includes how-to guides, readme files, Dockerfiles, and examples that help users get started with various features in WML CE. This resource is valuable for those looking to leverage IBM's machine learning tools effectively.
The [Bias Mitigation of Machine Learning Models Using AIF360](https://github.com/IBM/bias-mitigation-of-machine-learning-models-using-aif360) repository provides resources for analyzing data using RStudio, Jupyter, and Python in a collaborative environment. It utilizes the AI Fairness 360 toolkit, an open-source library offering metrics to check for unwanted bias in datasets and machine learning models, along with algorithms to mitigate such bias. This repository is essential for developers aiming to ensure fairness in their AI systems.
https://github.com/IBM/bias-mitigation-of-machine-learning-models-using-aif360
The [Machine Learning Models with AutoAI](https://github.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI) repository guides users through configuring AutoAI experiments and discusses classification models created using IBM's AutoAI. It provides step-by-step instructions for uploading datasets, associating machine learning services, and creating experiments, making it a practical resource for those new to AutoAI.
https://github.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI
The [IBM AI Engineering Specialization](https://github.com/11a55an/IBM-AI-Engineering-Specialization) repository contains resources and projects related to the IBM AI Engineering Specialization. It offers a structured learning path covering various aspects of AI, including machine learning, deep learning, and natural language processing. Through hands-on projects and curated content, this specialization equips learners with the knowledge and tools necessary to excel in AI engineering.
https://github.com/11a55an/IBM-AI-Engineering-Specialization
The [IBM Applied AI Professional Certificate](https://github.com/Ebadm/IBM-Applied-AI-Professional-Certificate) repository contains projects and exercises associated with the IBM Applied AI Professional Certificate. This program focuses on practical applications of AI, including learning Python, building chatbots, exploring machine learning and computer vision, and leveraging IBM Watson. It's designed to equip learners with essential skills to implement artificial intelligence using IBM Watson AI services.
https://github.com/Ebadm/IBM-Applied-AI-Professional-Certificate
The [Machine Learning IBM](https://github.com/worklifesg/Machine-Learning-IBM) repository offers materials on machine learning using Python 3.8.3 with Visual Studio Code. It includes programs from IBM's Machine Learning course and additional algorithms presented for learning purposes. Topics covered include regression, classification, clustering, and recommender systems, providing a comprehensive resource for those studying machine learning.
[Apache SystemDS](https://github.com/apache/systemds), formerly known as SystemML, is an open-source machine learning system for the end-to-end data science lifecycle. Developed by the Apache Software Foundation and IBM, SystemDS offers algorithm customizability via R-like and Python-like languages and supports multiple execution modes, including Standalone, Spark Batch, and Hadoop Batch. It provides automatic optimization based on data and cluster characteristics to ensure both efficiency and scalability.
The [AI Privacy Toolkit](https://github.com/IBM/ai-privacy-toolkit) is a collection of tools and techniques focused on the privacy and compliance of AI models. It includes methods for anonymizing machine learning model training data, ensuring that retrained models maintain data privacy standards. This toolkit is essential for developers aiming to build ethical and privacy-compliant AI systems.
The [AI Hardware Acceleration Kit (AIHWKit)](https://github.com/IBM/aihwkit) is a Python library that simulates the inference and training of deep neural networks using analog in-memory computing hardware. It provides tools for adapting deep neural networks to achieve equivalent accuracy on hardware with noisy and non-linear device characteristics, supporting the development of efficient AI hardware solutions.
https://github.com/IBM/aihwkit
The IBM Watson Machine Learning Samples repository offers a collection of notebooks that demonstrate the capabilities of Watson Machine Learning and watsonx.ai. These samples cover various aspects, including AutoAI, deep learning, and the deployment of models such as scikit-learn and XGBoost. They serve as practical guides for users of IBM's AI platforms, providing hands-on examples to facilitate understanding and implementation.
The IBM PowerAI repository contains ancillary and supplemental information to assist users of Watson Machine Learning Community Edition (WML CE). It includes how-to guides, readme files, Dockerfiles, and examples that help users get started with various features in WML CE. This resource is valuable for those looking to leverage IBM's machine learning tools effectively.
The Bias Mitigation of Machine Learning Models Using AIF360 repository provides resources for analyzing data using RStudio, Jupyter, and Python in a collaborative environment. It utilizes the AI Fairness 360 toolkit, an open-source library offering metrics to check for unwanted bias in datasets and machine learning models, along with algorithms to mitigate such bias. This repository is essential for developers aiming to ensure fairness in their AI systems.
https://github.com/IBM/bias-mitigation-of-machine-learning-models-using-aif360
The Machine Learning Models with AutoAI repository guides users through configuring AutoAI experiments and discusses classification models created using IBM's AutoAI. It provides step-by-step instructions for uploading datasets, associating machine learning services, and creating experiments, making it a practical resource for those new to AutoAI.
https://github.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI
The IBM AI Engineering Specialization repository contains resources and projects related to the IBM AI Engineering Specialization. It offers a structured learning path covering various aspects of AI, including machine learning, deep learning, and natural language processing. Through hands-on projects and curated content, this specialization equips learners with the knowledge and tools necessary to excel in AI engineering.
https://github.com/11a55an/IBM-AI-Engineering-Specialization
The IBM Applied AI Professional Certificate repository contains projects and exercises associated with the IBM Applied AI Professional Certificate. This program focuses on practical applications of AI, including learning Python, building chatbots, exploring machine learning and computer vision, and leveraging IBM Watson. It's designed to equip learners with essential skills to implement artificial intelligence using IBM Watson AI services.
https://github.com/Ebadm/IBM-Applied-AI-Professional-Certificate
The Machine Learning IBM repository offers materials on machine learning using Python 3.8.3 with Visual Studio Code. It includes programs from IBM's Machine Learning course and additional algorithms presented for learning purposes. Topics covered include regression, classification, clustering, and recommender systems, providing a comprehensive resource for those studying machine learning.
Apache SystemDS, formerly known as SystemML, is an open-source machine learning system for the end-to-end data science lifecycle. Developed by the Apache Software Foundation and IBM, SystemDS offers algorithm customizability via R-like and Python-like languages and supports multiple execution modes, including Standalone, Spark Batch, and Hadoop Batch. It provides automatic optimization based on data and cluster characteristics to ensure both efficiency and scalability.
The AI Privacy Toolkit is a collection of tools and techniques focused on the privacy and compliance of AI models. It includes methods for anonymizing machine learning model training data, ensuring that retrained models maintain data privacy standards. This toolkit is essential for developers aiming to build ethical and privacy-compliant AI systems.
The AI Hardware Acceleration Kit (AIHWKit) is a Python library that simulates the inference and training of deep neural networks using analog in-memory computing hardware. It provides tools for adapting deep neural networks to achieve equivalent accuracy on hardware with noisy and non-linear device characteristics, supporting the development of efficient AI hardware solutions.
https://github.com/IBM/aihwkit
The IBM Watson Machine Learning Samples repository offers a collection of notebooks that demonstrate the capabilities of Watson Machine Learning and watsonx.ai. These samples cover various aspects, including AutoAI, deep learning, and the deployment of models such as scikit-learn and XGBoost. They serve as practical guides for users of IBM's AI platforms, providing hands-on examples to facilitate understanding and implementation.
The IBM PowerAI repository contains ancillary and supplemental information to assist users of Watson Machine Learning Community Edition (WML CE). It includes how-to guides, readme files, Dockerfiles, and examples that help users get started with various features in WML CE. This resource is valuable for those looking to leverage IBM's machine learning tools effectively.
The Bias Mitigation of Machine Learning Models Using AIF360 repository provides resources for analyzing data using RStudio, Jupyter, and Python in a collaborative environment. It utilizes the AI Fairness 360 toolkit, an open-source library offering metrics to check for unwanted bias in datasets and machine learning models, along with algorithms to mitigate such bias. This repository is essential for developers aiming to ensure fairness in their AI systems.
https://github.com/IBM/bias-mitigation-of-machine-learning-models-using-aif360
The Machine Learning Models with AutoAI repository guides users through configuring AutoAI experiments and discusses classification models created using IBM's AutoAI. It provides step-by-step instructions for uploading datasets, associating machine learning services, and creating experiments, making it a practical resource for those new to AutoAI.
https://github.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI
The IBM AI Engineering Specialization repository contains resources and projects related to the IBM AI Engineering Specialization. It offers a structured learning path covering various aspects of AI, including machine learning, deep learning, and natural language processing. Through hands-on projects and curated content, this specialization equips learners with the knowledge and tools necessary to excel in AI engineering.
https://github.com/11a55an/IBM-AI-Engineering-Specialization
The IBM Applied AI Professional Certificate repository contains projects and exercises associated with the IBM Applied AI Professional Certificate. This program focuses on practical applications of AI, including learning Python, building chatbots, exploring machine learning and computer vision, and leveraging IBM Watson. It's designed to equip learners with essential skills to implement artificial intelligence using IBM Watson AI services.
https://github.com/Ebadm/IBM-Applied-AI-Professional-Certificate
The Machine Learning IBM repository offers materials on machine learning using Python 3.8.3 with Visual Studio Code. It includes programs from IBM's Machine Learning course and additional algorithms presented for learning purposes. Topics covered include regression, classification, clustering, and recommender systems, providing a comprehensive resource for those studying machine learning.
Apache SystemDS, formerly known as SystemML, is an open-source machine learning system for the end-to-end data science lifecycle. Developed by the Apache Software Foundation and IBM, SystemDS offers algorithm customizability via R-like and Python-like languages and supports multiple execution modes, including Standalone, Spark Batch, and Hadoop Batch. It provides automatic optimization based on data and cluster characteristics to ensure both efficiency and scalability.
The AI Privacy Toolkit is a collection of tools and techniques focused on the privacy and compliance of AI models. It includes methods for anonymizing machine learning model training data, ensuring that retrained models maintain data privacy standards. This toolkit is essential for developers aiming to build ethical and privacy-compliant AI systems.
The AI Hardware Acceleration Kit (AIHWKit) is a Python library that simulates the inference and training of deep neural networks using analog in-memory computing hardware. It provides tools for adapting deep neural networks to achieve equivalent accuracy on hardware with noisy and non-linear device characteristics, supporting the development of efficient AI hardware solutions.