#other #awesome #awesome_list #data_mining #deep_learning #explainability #interpretability #large_scale_machine_learning #large_scale_ml #machine_learning #machine_learning_operations #ml_operations #ml_ops #mlops #privacy_preserving #privacy_preserving_machine_learning #privacy_preserving_ml #production_machine_learning #production_ml #responsible_ai

This repository provides a comprehensive list of open-source libraries and tools for deploying, monitoring, versioning, scaling, and securing machine learning models in production. Here are the key benefits The repository includes a wide range of tools categorized into sections such as adversarial robustness, agentic workflow, AutoML, computation load distribution, data labelling and synthesis, data pipelines, data storage optimization, data stream processing, deployment and serving, evaluation and monitoring, explainability and fairness, feature stores, and more.

- **Production Readiness** The repository is actively maintained and contributed to by a community of developers, ensuring that the tools are up-to-date and well-supported.

- **Ease of Use** Tools for optimized computation, model storage optimization, and neural search and retrieval help in improving the performance and efficiency of machine learning models.

- **Privacy and Security**: Libraries focused on privacy and security, such as federated learning and homomorphic encryption, ensure that sensitive data is protected during model training and deployment.

Using this repository, you can streamline your machine learning workflows, improve model performance, and ensure robustness and security in your production environments.

https://github.com/EthicalML/awesome-production-machine-learning

#other #ai #data_science #devops #engineering #federated_learning #machine_learning #ml #mlops #software_engineering

This resource is a comprehensive guide to Machine Learning Operations (MLOps), providing a wide range of tools, articles, courses, and communities to help you manage and deploy machine learning models effectively.

**Key Benefits** Access to numerous books, articles, courses, and talks on MLOps, machine learning, and data science.
- **Community Support** Detailed guides on workflow management, feature stores, model deployment, testing, monitoring, and maintenance.
- **Infrastructure Tools** Resources on model governance, ethics, and responsible AI practices.

Using these resources, you can improve your skills in designing, training, and running machine learning models efficiently, ensuring they are reliable, scalable, and maintainable in production environments.

https://github.com/visenger/awesome-mlops

#cplusplus #deep_learning #deep_neural_networks #distributed #machine_learning #ml #neural_network #python #tensorflow

TensorFlow is a powerful tool for machine learning that helps you build and deploy AI applications easily. It was developed by Google and is now open source, meaning anyone can use and contribute to it. TensorFlow provides tools, libraries, and a strong community to support your work. You can install it using Python with a simple command like `pip install tensorflow`, and it supports various devices including GPUs. This makes it versatile for researchers and developers alike, allowing you to push the boundaries of machine learning and create innovative applications.

https://github.com/tensorflow/tensorflow

#cplusplus #accelerator #llama #llm #low_level_programming #metal #mistral #mixtral #ml #resnet #stable_diffusion #tenstorrent

Tenstorrent's TT-Metal is a powerful tool for developing AI models. It allows users to create custom kernels for their hardware, which can improve performance by reducing memory usage. This is especially useful for large language models (LLMs) like Llama and Mixtral. The TT-Metal system supports efficient data movement and computation, making it beneficial for users who need to run complex AI tasks quickly and effectively. By optimizing how data is stored and processed, TT-Metal helps users achieve better results with less effort.

https://github.com/tenstorrent/tt-metal

#cplusplus #arm #convolution #deep_learning #embedded_devices #llm #machine_learning #ml #mnn #transformer #vulkan #winograd_algorithm

MNN is a lightweight and efficient deep learning framework that helps run AI models on mobile devices and other small devices. It supports many types of AI models and can handle tasks like image recognition and language processing quickly and locally on your device. This means you can use AI features without needing to send data to the cloud, which improves privacy and speed. MNN is used in many apps, including those from Alibaba, and supports various platforms like Android and iOS. It also helps reduce the size of AI models, making them faster and more efficient.

https://github.com/alibaba/MNN

#rust #ai #ml #zk #zk_snarks #zkml

DeepProve is a fast and efficient tool that uses zero-knowledge cryptography to prove that neural network inferences (like those from MLPs or CNNs) are done correctly without revealing any private data or the model itself. It speeds up verification significantly, for example, proving CNN inference 158 times faster and dense layers 54 times faster than previous methods. This technology is especially useful in fields like healthcare, finance, and blockchain, where privacy and trust are crucial, allowing you to verify AI results securely without exposing sensitive information or proprietary models. This means you get trustworthy AI verification while keeping data confidential.

https://github.com/Lagrange-Labs/deep-prove

#rust #ai #ai_engineering #anthropic #artificial_intelligence #deep_learning #genai #generative_ai #gpt #large_language_models #llama #llm #llmops #llms #machine_learning #ml #ml_engineering #mlops #openai #python #rust

TensorZero is a free, open-source tool that helps you build and improve large language model (LLM) applications by using real-world data and feedback. It gives you one simple API to connect with all major LLM providers, collects data from your app’s use, and lets you easily test and improve prompts, models, and strategies. You can see how your LLMs perform, compare different options, and make them smarter, faster, and cheaper over time—all while keeping your data private and under your control. This means you get better results with less effort and cost, and your apps keep improving as you use them[1][2][3].

https://github.com/tensorzero/tensorzero

#other #automl #chatgpt #data_analysis #data_science #data_visualization #data_visualizations #deep_learning #gpt #gpt_3 #jax #keras #machine_learning #ml #nlp #python #pytorch #scikit_learn #tensorflow #transformer

This is a comprehensive, regularly updated list of 920 top open-source Python machine learning libraries, organized into 34 categories like frameworks, data visualization, NLP, image processing, and more. Each project is ranked by quality using GitHub and package manager metrics, helping you find the best tools for your needs. Popular libraries like TensorFlow, PyTorch, scikit-learn, and Hugging Face transformers are included, along with specialized ones for time series, reinforcement learning, and model interpretability. This resource saves you time by guiding you to high-quality, actively maintained libraries for building, optimizing, and deploying machine learning models efficiently.

https://github.com/ml-tooling/best-of-ml-python

#html #data_science #education #machine_learning #machine_learning_algorithms #machinelearning #machinelearning_python #microsoft_for_beginners #ml #python #r #scikit_learn #scikit_learn_python

Microsoft’s "Machine Learning for Beginners" is a free, 12-week course with 26 lessons designed to teach classic machine learning using Python and Scikit-learn. It includes quizzes, projects, and assignments to help you learn by doing, with lessons themed around global cultures to keep it engaging. You can access solutions, videos, and even R language versions. The course is beginner-friendly, flexible, and helps build practical skills step-by-step, making it easier to understand and apply machine learning concepts in real-world scenarios. This structured approach boosts your learning retention and prepares you for further study or career growth in ML[1][5].

https://github.com/microsoft/ML-For-Beginners

#html #ech #fail2ban #http #mixed #ml_dsa_65 #ml_kem_768 #post_quantum #reality #shadowsocks #shadowsocks2022 #tls #trojan #tunnel #vless #vmess #wireguard #x25519 #xtls_rprx_vision #xtls_rprx_vision_udp443

3X-UI is an easy-to-use, open-source web control panel for managing Xray-core VPN servers. It supports many VPN protocols like VMess, VLESS, Shadowsocks, Trojan, and WireGuard, letting you configure, monitor traffic, manage users, and set limits through a simple web interface. It includes features like automatic SSL certificates, traffic statistics, multi-user support, and security options such as firewall rules and IP bans. This makes managing VPN servers faster, more secure, and accessible even if you are not an expert, helping you control your VPN setup efficiently and safely.

https://github.com/MHSanaei/3x-ui