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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, Cisco's MindMeld is an open-source Conversational AI platform designed for building advanced voice interfaces and chatbots. Written in Python, it provides comprehensive tools for natural language processing, including domain classification, intent classification, entity recognition, and role labeling. MindMeld facilitates the development of applications with deep understanding in specific domains, offering capabilities for dialogue management and custom knowledge base creation.
The Cisco-IE Telemetry repository, established in 2018, offers open-source datasets for researchers and developers focusing on network anomaly detection using machine learning. These real-world datasets support the development, testing, and comparison of both supervised and unsupervised anomaly detection algorithms, aiding in the advancement of network monitoring and security solutions.
Launched in 2021, ResponsibleAI is a Python library developed by Cisco to assist AI developers in responsible AI development. It provides a core API and a web-based dashboard application to measure various metrics throughout the AI development lifecycle, including data quality assessment, model performance, fairness, and robustness. The toolkit also offers interactive tools for model explanation and analysis, promoting ethical AI practices.
In 2022, Cisco introduced Flame, a federated learning system designed for edge computing with flexibility and scalability. Comprising a service (control plane) and a Python library (data plane), Flame enables developers to compose and deploy federated learning training workloads efficiently. It supports various algorithms and mechanisms, including FedAvg, FedYogi, and FedProx, facilitating collaborative machine learning across decentralized data sources.
Released in 2023, ModelSmith is a toolkit developed by Cisco for compressing machine learning models to enhance speed, reduce size, and improve energy efficiency. It offers features such as post-training quantization, machine unlearning, and various pruning algorithms, making it suitable for deployment across diverse devices and platforms while maintaining satisfactory performance.
Cisco's Cognitive platform, introduced in 2017, provides a machine learning as a service solution. It facilitates the deployment of machine learning models through a user-friendly interface, supporting CSV input data and offering API documentation via Swagger. The platform aims to simplify the integration of machine learning capabilities into applications, promoting accessibility for users with varying levels of expertise.
The Multi-Task Learning Library, developed by Cisco in 2022, is a Python library designed to simplify multi-task learning for computer vision models with shared backbones. It enables the training of models that can perform multiple tasks simultaneously, optimizing resource utilization and improving performance across tasks such as object detection and segmentation.
In 2022, Cisco Talos released a repository containing code, datasets, and technical information related to their research on binary function similarity using machine learning. This work, presented at USENIX Security '22, explores how machine learning can address challenges in binary function similarity, contributing to advancements in cybersecurity and malware analysis.
The CiscoAI Reference repository, established in 2018, provides reference implementations of MLPerf benchmarks. These implementations serve as standardized evaluations for machine learning performance, enabling researchers and developers to assess and compare the efficiency of different hardware and software configurations in executing machine learning tasks.
The Cisconetwork Machine-Learning repository, created in 2020, offers a collection of Python scripts demonstrating various machine learning algorithms, including decision trees, linear regression, and support vector regression. These examples serve as practical resources for individuals learning about machine learning techniques and their applications.
https://github.com/Cisconetwork/Machine-Learning
The Cisco AI organization on GitHub serves as a centralized hub for Cisco's artificial intelligence projects. It hosts repositories related to machine learning benchmarks, hyperparameter tuning, and machine learning toolkits for Kubernetes. This collection reflects Cisco's commitment to advancing AI technologies and providing resources for the developer community.
Introduced in 2018, MindMeld is an open-source Conversational AI platform developed by Cisco. It enables the creation of advanced voice interfaces and chatbots with deep domain understanding. The platform offers tools for natural language processing, including domain classification, intent classification, entity recognition, and role labeling, facilitating the development of sophisticated conversational applications.
The Telemetry repository, established in 2018 by Cisco, provides open-source datasets for network anomaly detection research. These real-world datasets assist in developing, testing, and comparing machine learning algorithms for anomaly detection, supporting advancements in network monitoring and security.
Launched in 2021, ResponsibleAI is a Python library created by Cisco to aid developers in responsible AI development. It offers a core API and a web-based dashboard to measure metrics throughout the AI development lifecycle, including data quality, model performance, fairness, and robustness, promoting ethical AI practices.
In 2022, Cisco introduced Flame, a federated learning system designed for edge computing with flexibility and scalability. Comprising a service (control plane) and a Python library (data plane), Flame enables developers to compose and deploy federated learning training workloads efficiently, supporting various algorithms and mechanisms.
Released in 2023, ModelSmith is a toolkit developed by Cisco for compressing machine learning models to enhance speed, reduce size, and improve energy efficiency. It features post-training quantization, machine unlearning, and various pruning algorithms, making it suitable for deployment across diverse devices and platforms.
Cognitive, introduced by Cisco in 2017, is a platform offering machine learning as a service. It facilitates the deployment of machine learning models through a user-friendly interface, supporting CSV input data and providing API documentation via Swagger, simplifying the integration of machine learning capabilities into applications.
Developed by Cisco in 2022, the Multi-Task Learning Library is a Python library that simplifies multi-task learning for computer vision models with shared backbones. It enables training models to perform multiple tasks simultaneously, optimizing resource utilization and improving performance across tasks such as object detection and segmentation.
In 2022, Cisco Talos released a repository containing code, datasets, and technical information related to their research on binary function similarity using machine learning. This work, presented at USENIX Security '22, explores how machine learning can address challenges in binary function similarity, contributing to advancements in cybersecurity and malware analysis.
The CiscoAI Reference repository, established in 2018, provides reference implementations of MLPerf benchmarks. These implementations serve as standardized evaluations for machine learning performance, enabling researchers and developers to assess and compare the efficiency of different hardware and software configurations in executing machine learning tasks.