Return to AI-DL-ML-LLM GitHub, AI-DL-ML-LLM Focused Companies, Hugging Face AI-DL-ML-LLM Services, AWS AI-DL-ML-LLM Services, Azure AI-DL-ML-LLM Services, GCP AI-DL-ML-LLM Services, IBM Cloud AI-DL-ML-LLM Services, Oracle Cloud AI-DL-ML-LLM Services, OpenAI AI-DL-ML-LLM Services, NVIDIA AI-DL-ML-LLM Services, Intel AI-DL-ML-LLM Services, Kubernetes AI-DL-ML-LLM Services, Apple AI-DL-ML-LLM Services, Meta-Facebook AI-DL-ML-LLM Services, Cisco AI-DL-ML-LLM Services
For the top 15 GitHub repos, ask for 10 paragraphs. e.g. Amazon SageMaker Features, Amazon SageMaker Alternatives, Amazon SageMaker Security, , Amazon SageMaker DevOps
DeepSpeed, introduced in May 2020, is an open-source deep learning optimization library for PyTorch. It is designed to reduce computing power and memory usage, enabling the training of large distributed models with improved parallelism on existing hardware. DeepSpeed includes features like mixed precision training and the Zero Redundancy Optimizer (ZeRO), facilitating the training of models with over a trillion parameters.
Neural Network Intelligence (NNI), launched in 2018, is an open-source AutoML toolkit developed by Microsoft Research. It automates feature engineering, model compression, neural architecture search, and hyper-parameter tuning, supporting various machine learning tasks and facilitating efficient model development.
Infer.NET, released in 2008, is a .NET software library for machine learning, supporting Bayesian inference in graphical models and probabilistic programming. Developed by Microsoft Research, it has been utilized in applications across bioinformatics, epidemiology, and computer vision. In 2018, Infer.NET was open-sourced under the MIT License.
ML.NET, introduced in May 2018, is a free, cross-platform, open-source machine learning framework for .NET languages like C# and F#. It enables developers to build, train, and deploy custom machine learning models, supporting tasks such as classification, regression, and anomaly detection.
The LLMOps Workshop is a comprehensive course designed to guide users through building, evaluating, monitoring, and deploying large language model solutions efficiently using Azure AI, Azure Machine Learning Prompt Flow, Content Safety, and Azure OpenAI. It aims to equip practitioners with the skills necessary for effective LLM operations.
The Azure Multimodal AI & LLM Processing Accelerator is a customizable code template for building and deploying production-grade data processing pipelines that incorporate Azure AI services and Azure OpenAI/AI Studio LLM models. It facilitates the creation of complex, reliable, and accurate pipelines for real-world use cases.
https://github.com/Azure/multimodal-ai-llm-processing-accelerator
GenAIScript is a framework that enables the automation of generative AI scripting. It allows users to define agents and tools that can interact with repositories and external APIs, facilitating tasks such as statistical analysis of commits and data retrieval.
The Purview Machine Learning Lineage Solution Accelerator provides a framework for capturing and visualizing the lineage of machine learning models and data. It integrates with Azure Machine Learning and Azure Purview to enhance transparency and traceability in AI workflows.
https://github.com/microsoft/Purview-Machine-Learning-Lineage-Solution-Accelerator
The AI Utilities repository offers a collection of tools and utilities designed to assist in AI and machine learning projects. It includes scripts and resources for data processing, model evaluation, and deployment, streamlining the development process.
RobustLearn is a project focused on robust machine learning for responsible AI. It provides resources and tools aimed at enhancing the reliability and fairness of AI models, contributing to the development of ethical AI systems.
https://github.com/microsoft/robustlearn
Project Turing, introduced in 2019, is a collection of pretrained Natural Language Processing (NLP) models developed by Microsoft for tasks like text generation, translation, and summarization. It includes models like Turing-NLG, one of the largest language models in the world.
SynapseML, launched in 2021, is an open-source library for building scalable and distributed machine learning pipelines. It integrates with Spark and supports tasks like Deep Learning (DL), Natural Language Processing (NLP), and computer vision.
Microsoft SEAL, introduced in 2015, is an open-source library for homomorphic encryption. It enables secure computations on encrypted data, supporting privacy-preserving AI applications in healthcare, finance, and more.
InterpretML, launched in 2019, provides tools for understanding and interpreting machine learning models. It supports explainable AI by offering insights into model predictions through techniques like SHAP and LIME.
LightGBM, introduced in 2016, is a gradient boosting framework designed for high performance and scalability. It is widely used for classification, regression, and ranking tasks in Machine Learning (ML) projects.
Vowpal Wabbit (VW), acquired by Microsoft in 2015, is an efficient and fast learning system for online and large-scale learning. It supports a wide range of Machine Learning (ML) algorithms and applications.
AirSim, launched in 2017, is an open-source simulator for autonomous vehicles and drones. It is designed for research and development in robotics and reinforcement learning, offering realistic environments for testing.
FARM, introduced in 2019, is a framework for building and training scalable NLP models. It simplifies the creation of transformer-based systems for tasks like question answering, entity recognition, and sentiment analysis.
Project Bonsai, launched in 2018, is a platform for building and deploying reinforcement learning models in industrial automation. It simplifies the integration of AI into manufacturing, logistics, and control systems.
Hummingbird, introduced in 2020, is a library that translates traditional Machine Learning (ML) models into tensor computations. It allows for the deployment of ML models on hardware accelerators like GPUs, enhancing performance.
https://github.com/microsoft/hummingbird
AI for Earth, launched in 2017, is an initiative and open-source platform for applying AI to environmental challenges. It includes tools and datasets for tasks like species monitoring, land-use classification, and climate modeling.
Recommenders, introduced in 2018, is an open-source library for building and evaluating recommendation systems. It includes scalable algorithms like collaborative filtering, deep learning, and content-based recommendations.
TextWorld, launched in 2018, is a reinforcement learning environment for text-based games. It allows researchers to train AI agents on tasks requiring language understanding and decision-making in narrative contexts.
Dapr, introduced in 2020, is a distributed application runtime for building event-driven, serverless, and microservice applications. It integrates with AI workflows to simplify state management and communication.
Icebreaker, launched in 2019, is a chatbot framework designed to facilitate team introductions and interactions within organizations. It leverages AI to promote collaboration and engagement in remote work settings.
https://github.com/microsoft/botframework-solutions/tree/main/skills/icebreaker
Quantum Development Kit (QDK), introduced in 2017, provides tools for developing quantum algorithms and applications. It includes the Q# programming language and libraries for quantum Machine Learning (ML) and cryptography.
RocketSim, launched in 2020, is a reinforcement learning environment for aerospace applications. It enables training and simulation of AI models for control systems in rockets, drones, and satellites.
Cognitive Toolkit (CNTK), introduced in 2016, is a deep learning framework for training and evaluating neural networks. It supports tasks like image recognition and speech processing with optimized parallelization.
AI Fairness 360 Toolkit, launched in 2020, is a library for detecting and mitigating bias in Machine Learning (ML) models. It provides metrics and algorithms to ensure fairness and transparency in AI systems.
AI Builder, introduced in 2019, is a low-code platform for creating custom AI models. It integrates with Microsoft Power Platform to automate workflows and enhance decision-making across various business applications.
https://github.com/microsoft/ai-builder
Conversational AI Framework, introduced in 2019, provides tools for building, training, and deploying intelligent bots. It integrates with Azure Bot Service and LUIS to support natural language understanding and conversation management.
Project Silica, launched in 2019, explores the use of AI and ultrafast lasers for storing data in quartz glass. It is designed for long-term data archiving, leveraging AI models for encoding and retrieval.
Low-Rank Adaptation (LoRA), introduced in 2021, is a technique for fine-tuning large Language Models (LLMs) efficiently. It reduces memory and computational requirements by focusing on parameter-efficient updates.
EdgeAI Toolkit, launched in 2020, provides resources for deploying AI models on edge devices. It supports scenarios like object detection, anomaly detection, and predictive maintenance in IoT environments.
ZeRO-Inference, introduced in 2021, is an optimization framework for deploying Large Language Models (LLMs) efficiently in production. It minimizes memory requirements while maintaining high throughput.
https://github.com/microsoft/DeepSpeedExamples/tree/master/ZeRO-Inference
AI Development Acceleration Program, launched in 2022, provides pre-built templates and tools for building enterprise-grade AI applications. It includes modules for natural language processing, predictive analytics, and computer vision.
Unified Text Embeddings, introduced in 2020, is a library for generating high-quality embeddings for text and multimodal data. It supports tasks like document retrieval, sentiment analysis, and clustering.
AI Noise Suppression, launched in 2020, is a tool for improving audio quality in real-time communication. It uses Deep Learning (DL) to remove background noise from voice calls and recordings.
Project Tonic, introduced in 2021, focuses on improving AI system energy efficiency. It provides tools for optimizing resource usage in large-scale Machine Learning (ML) training and inference.
Embodied AI Toolkit, launched in 2021, supports research in embodied Artificial Intelligence (AI), enabling agents to interact with physical or simulated environments. It includes tasks like navigation, manipulation, and language grounding.
https://github.com/microsoft/embodied-ai-toolkit
Project Alexandria, introduced in 2021, is a framework for building knowledge graphs using AI and Machine Learning (ML). It integrates structured and unstructured data to enable advanced search, reasoning, and analytics.
Cognitive Services Speech SDK, launched in 2018, provides APIs for speech-to-text, text-to-speech, and translation. It integrates with Azure for deploying real-time and batch processing AI speech applications.
Project ORBIT, introduced in 2020, is an open-source framework for building edge intelligence applications. It combines AI and IoT to support predictive maintenance, energy optimization, and real-time decision-making.
SEEDS, launched in 2021, is a research initiative focusing on optimizing the energy efficiency of Deep Learning (DL) models. It includes tools for evaluating and improving power consumption during training and inference.
Adaptive Learning Platform, introduced in 2019, supports personalized learning experiences using AI. It enables adaptive content recommendations and real-time progress tracking in educational applications.
AI Anomaly Detection Toolkit, launched in 2018, is a library for identifying anomalies in time-series data. It supports applications like fraud detection, predictive maintenance, and monitoring system health.
Mixed Reality Toolkit (MRTK), introduced in 2017, is an open-source framework for building immersive mixed reality applications. It integrates AI to enhance spatial awareness, object recognition, and user interaction.
Azure Quantum Optimization Services, launched in 2020, provides tools for solving optimization problems using quantum-inspired AI algorithms. It is applied in logistics, finance, and supply chain management.
Responsible AI Toolkit, introduced in 2021, offers tools for building fair, transparent, and accountable AI systems. It includes resources for detecting and mitigating biases in machine learning models.
AI-driven Accessibility Toolkit, launched in 2019, provides tools for creating inclusive applications using AI. It focuses on improving accessibility features like real-time captions, screen readers, and gesture recognition.
https://github.com/microsoft/accessibility-toolkit
Project Moab, introduced in 2020, is an open-source balancing robot platform designed for learning and experimenting with reinforcement learning techniques. It provides a hands-on way to explore AI and control systems.
RocketML, launched in 2021, is a scalable platform for distributed machine learning and data processing. It enables high-speed training and inference across large datasets using advanced AI pipelines.
AI for Healthcare, introduced in 2019, provides tools and datasets for building machine learning models in the medical domain. It supports applications like predictive analytics, disease diagnosis, and treatment recommendation systems.
AI Builder for Dynamics 365, launched in 2019, integrates low-code AI tools into Dynamics 365. It enables business users to create models for tasks like sentiment analysis, object detection, and data predictions.
AI for Accessibility, introduced in 2018, is a repository of tools and projects focused on leveraging AI to empower people with disabilities. It supports solutions like speech-to-text transcription, gesture recognition, and accessibility design.
HoloLens AI Toolkit, launched in 2016, provides tools for integrating AI models into mixed reality applications. It supports object recognition, spatial understanding, and natural language interaction on HoloLens devices.
AI Data Wrangler, introduced in 2020, is a library for automating data preprocessing workflows. It simplifies data cleaning, transformation, and feature engineering for Machine Learning (ML) projects.
Project Acoustics, launched in 2019, uses AI to model real-time spatial audio in 3D environments. It enhances immersive experiences in gaming, virtual reality, and architectural design.
Azure Machine Learning CLI, introduced in 2018, is a command-line tool for managing machine learning workflows on Azure. It supports model training, deployment, and monitoring in a scalable environment.
Responsible AI Dashboard, launched in 2021, is an interactive visualization tool for assessing and improving the fairness, interpretability, and reliability of AI models. It integrates metrics for bias detection and explainability.
https://github.com/microsoft/responsible-ai-dashboard
Residual Policy Learning, introduced in 2020, is a framework for improving control policies in reinforcement learning by combining learned behaviors with existing knowledge. It accelerates the development of robust AI models in robotics and automation.
AI Accelerator Toolkit, launched in 2019, provides tools for optimizing Machine Learning (ML) models for deployment on hardware accelerators like GPUs and TPUs. It supports tasks like quantization, pruning, and mixed-precision training.
Project Torchaudio, introduced in 2020, is a library for audio processing built on PyTorch. It includes tools for speech recognition, sound classification, and audio augmentation, enhancing the development of audio-based AI applications.
Predictive Maintenance Toolkit, launched in 2018, is a set of resources for building AI solutions that predict equipment failures. It integrates machine learning models with IoT data streams to reduce downtime and maintenance costs.
Responsible NLP Toolkit, introduced in 2021, focuses on ensuring fairness, transparency, and accountability in Natural Language Processing (NLP). It includes bias detection and mitigation methods for language models.
Azure Cognitive Services Vision API, launched in 2017, provides pre-trained AI models for image and video analysis. It supports tasks like face detection, object recognition, and text extraction from images.
Game Stack AI, introduced in 2019, offers tools for incorporating AI features into video games. It includes resources for creating intelligent NPCs, procedural content generation, and player behavior analytics.
BioAI Toolkit, launched in 2020, is designed for applying AI to bioinformatics and genomics research. It provides models and datasets for tasks like gene sequence analysis, protein structure prediction, and drug discovery.
Virtual Assistant Toolkit, introduced in 2018, helps developers build conversational AI solutions. It integrates with the Azure Bot Framework and supports natural language understanding and multi-turn dialogues.
Object Recognition Toolkit, launched in 2021, provides models and tools for object detection and segmentation. It is optimized for edge devices and integrates with Azure IoT for real-time analytics.