#Model Deployment

The project provides a robust OCR library designed to equip developers with effective tools for model training. It includes features such as real-time layout parsing, low-code solutions to minimize costs, and diverse deployment options including high-performance inference and service-based deployment. Model integration is simplified through tools like PaddleX, offering broad model support via an easy-to-use Python API. Additionally, the project supports seamless adaptation across various hardware platforms, which enhances its application in tasks like text correction, layout detection, and formula recognition for industry-scale use.

This repository provides an extensive collection of open-source libraries to bolster production machine learning workflows. It covers crucial aspects such as deployment, monitoring, scaling, and security, across applications including AutoML, data pipeline optimization, adversarial robustness, and tools tailored for computer vision and NLP. Features include support for distributed computing and model tracking, ensuring efficient machine learning operations. Suitable for developers and researchers requiring dependable tools to optimize their machine learning processes.

AiDB provides a unified interface for deploying deep learning models in C++, compatible with frameworks like ONNXRUNTIME, MNN, NCNN, TNN, PaddleLite, and OpenVINO. Supporting platforms such as Linux, MacOS, and Android, it features demos in Python, Lua, and Go, aiming to simplify deployment with user-friendly and flexible designs. This toolbox is ideal for streamlining AI model deployment across diverse environments.

Access a wide range of AI models for computer vision, NLP, speech, and cross-modal tasks. Models are deployable with just three lines of code, compatible with Linux, Windows, and MacOS. Newest features include ERNIE-ViLG, Disco Diffusion, and Stable Diffusion. Utilize models as a service and explore resources on Hugging Face Space through interactive demos with available pre-trained open-source models.

Discover a broad collection of MLOps resources featuring courses, articles, communities, and books. This resource list offers insights into essential elements of Machine Learning Operations, including workflow management, model deployment, testing, and infrastructure. It assists developers and product managers in optimizing ML products and procedures. Additionally, explore discussions on ethics, governance, and ML/AI economics to keep pace with industry changes. Stay informed about the latest trends and practices in the rapidly evolving MLOps field.

Llama-api-server provides a RESTful API compatible with OpenAI, leveraging open source technologies like llama and llama2, enabling integrations with various GPT tools and custom models. It offers setup and usage guides, supporting features such as completions, embeddings, and chat. Options for model preparation include llama.cpp and pyllama, with the server offering token authentication and performance configurations, making it a versatile alternative for AI development.

MLflow offers a versatile platform for machine learning, featuring APIs that integrate smoothly with libraries like TensorFlow and PyTorch. It provides capabilities such as experiment tracking with an interactive interface, reproducible code packaging, and deployment support on platforms like Docker and Spark. The model registry aids in managing the complete lifecycle of models collaboratively.

ChatTTS-ui provides a straightforward local web UI and API for text-to-speech conversion. It accommodates multilingual text and numeric integration for flexible voice synthesis. Compatible with both Windows and Linux, it offers deployment via pre-packaged or source versions. GPU acceleration is supported on NVIDIA cards, enabling efficient API usage. Features include streamlined installation, model management, and cross-device support, catering to different computational capabilities.

Discover the essential knowledge and skills for a computer vision algorithm engineer, including programming, machine learning, image recognition, and model deployment. This collection guides the growth journey with organized learning paths and insights into industry opportunities. Initially an internship guide, it has evolved into a technical repository with reduced updates; related content on deep learning and model inference is available elsewhere. Suitable for beginners or those aiming to excel in CV algorithm engineering.

Learn to efficiently deploy both official and Chinese LLaMA2 models with Docker for local use. This guide provides detailed instructions and scripts for setting up 7B and 13B models, suitable for GPU or CPU. Ideal for developers looking to test language models, it highlights the capabilities and advantages of using these models in different applications.

Sagify provides a streamlined interface to manage machine learning workflows on AWS SageMaker, enabling developers to focus on model building rather than infrastructure. Its modular architecture includes a Large Language Model (LLM) Gateway module, offering a unified API to seamlessly integrate multiple open source and proprietary language models into workflows. With compatibility for both OpenAI and open-source models, Sagify simplifies the deployment of models like Stable Diffusion and GPT variants. The platform supports chat completions, image creations, and embeddings, making it a comprehensive tool for leveraging machine learning capabilities efficiently with minimal coding. Discover detailed guidance through their comprehensive documentation.