Search Results for "deploy"
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Showing 246 open source projects for "deploy"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
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Seldon Core
An MLOps framework to package, deploy, monitor and manage models
...Framework agnostic, supports top ML libraries, toolkits and languages. Advanced deployments with experiments, ensembles and transformers. Our open-source framework makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes. -
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Appsmith
Low code project to build admin panels, internal tools, and dashboards
...Connect the data to the UI by configuring the components. Where needed, you can use the Javascript IDE to create more advanced features and data transformations - the sky is the limit! Deploy your app on our free, cloud-hosted version or to any platform of your choice - Docker, Kubernetes, AWS, DigitalOcean, Heroku, and more. -
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Bolt.new
Prompt, run, edit, and deploy full-stack web applications
Bolt.new is an AI-powered full-stack development platform created by StackBlitz that enables users to build, run, edit, and deploy complete web applications directly from the browser without requiring any local setup or traditional development environment. It operates as an intelligent coding agent where users describe what they want to build in natural language, and the system generates functional applications, including frontend, backend, and infrastructure components. -
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bolt.diy
Prompt, run, edit, & deploy full-stack web applications using any LLM
bolt.diy is an open-source platform that allows you to easily create, run, edit, and deploy full-stack web applications using a variety of large language models (LLMs). It supports popular models like OpenAI, Anthropic, Ollama, OpenRouter, Gemini, LMStudio, Mistral, xAI, HuggingFace, DeepSeek, and Groq, and provides the flexibility to integrate additional models through the Vercel AI SDK. Whether you’re experimenting with pre-built models or developing custom AI-driven applications, bolt.diy offers a smooth and intuitive experience for building AI-powered web apps. ... -
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
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OpenLLM
Operating LLMs in production
An open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease. With OpenLLM, you can run inference with any open-source large-language models, deploy to the cloud or on-premises, and build powerful AI apps. Built-in supports a wide range of open-source LLMs and model runtime, including Llama 2， StableLM, Falcon, Dolly, Flan-T5, ChatGLM, StarCoder, and more. Serve LLMs over RESTful API or gRPC with one command, query via WebUI, CLI, our Python/Javascript client, or any HTTP client. -
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Sim
Build, deploy, and orchestrate AI agents
Sim is an open-source platform designed to build, deploy, and orchestrate AI agent workflows through a unified intelligence layer. It positions itself as infrastructure for managing an “AI workforce,” enabling developers and teams to coordinate multiple agents and automate complex processes from a central environment. The project focuses on low-code and no-code accessibility while still supporting advanced customization through TypeScript and modern web tooling. -
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Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
...You don't have to be a machine learning/deep learning expert to get started. You can use your existing Java expertise as an on-ramp to learn and use machine learning and deep learning. You can use your favorite IDE to build, train, and deploy your models. DJL makes it easy to integrate these models with your Java applications. Because DJL is deep learning engine agnostic, you don't have to make a choice between engines when creating your projects. You can switch engines at any point. To ensure the best performance, DJL also provides automatic CPU/GPU choice based on hardware configuration. -
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Telegram File Stream Bot
A telegram bot that will give instant stream links for telegram files
A Telegram bot to generate direct link for your Telegram files. -
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OpenVINO
OpenVINO™ Toolkit repository
...Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks. Use models trained with popular frameworks like TensorFlow, PyTorch and more. Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. ... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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LlamaDeploy
Deploy your agentic worfklows to production
...Developers can define workflows that involve multiple steps such as data retrieval, reasoning, tool invocation, and response generation, then deploy them using the framework’s infrastructure tools. The design emphasizes scalability, modularity, and fault-tolerant execution so that agent systems can run reliably in production environments. -
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OpenVINO Notebooks
Jupyter notebook tutorials for OpenVINO
openvino_notebooks is a collection of interactive Jupyter notebooks designed to demonstrate how to build, optimize, and deploy artificial intelligence applications using the OpenVINO toolkit. The repository provides practical tutorials that guide developers through various AI workflows including computer vision, natural language processing, and generative AI tasks. Each notebook demonstrates how to run pre-trained models, optimize inference performance, and deploy models across hardware such as CPUs, GPUs, and specialized accelerators. ... -
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RF-DETR
RF-DETR is a real-time object detection and segmentation
...The model is designed to detect objects and segment them within images or video streams using a unified detection pipeline. RF-DETR emphasizes strong performance across both accuracy and latency benchmarks, allowing developers to deploy high-quality detection models in applications that require immediate processing such as robotics, autonomous systems, and industrial inspection. The repository includes Python packages, training scripts, and model configurations that enable researchers and engineers to train and deploy detection models on custom datasets. -
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Beelzebub
A secure low code honeypot framework
...Honeypots are systems intentionally exposed to attackers in order to capture malicious behavior, and Beelzebub enhances this concept by incorporating artificial intelligence and virtualization techniques. The platform allows organizations and researchers to deploy decoy services that mimic real infrastructure while recording attacker interactions. By using AI models to simulate realistic system behavior, the honeypot becomes harder for attackers to identify, increasing the likelihood that malicious activity can be observed and analyzed. The framework is designed with a low-code configuration approach so security teams can easily deploy honeypots for multiple services and ports. -
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TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
...It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model using a single language throughout the lifecycle of model exploration and production. TFP is open source and available on GitHub. Tools to build deep probabilistic models, including probabilistic layers and a `JointDistribution` abstraction. Variational inference and Markov chain Monte Carlo. A wide selection of probability distributions and bijectors. ... -
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Kalavai
Turn everyday devices into your own AI cluster
Kalavai is a self-hosted platform that turns everyday devices into your very own AI cluster. Do you have an old desktop or a gaming laptop gathering dust? Aggregate resources from multiple machines and say goodbye to CUDA out-of-memory errors. Deploy your favorite open-source LLM, fine-tune it with your own data, or simply run your distributed work, zero-DevOps. Simple. Private. Yours. -
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Gemini Next Chat
Deploy your private Gemini application for free with one click
Gemini Next Chat is an open-source web application that allows you to deploy your own private chat interface powered by Google’s Gemini models (e.g., Gemini 1.5, Gemini 2.0, etc.). It is built with Next.js/TypeScript and targets developers and hobbyists who want a self-hosted solution for interacting with advanced multimodal models (text, image, voice). It supports features like image recognition, voice-based conversation, plugins (web search, ArXiv search, weather, etc.), and client apps (tray app) for greater convenience. ... -
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Infinity
Low-latency REST API for serving text-embeddings
Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. Infinity is developed under MIT License. Infinity powers inference behind Gradient.ai and other Embedding API providers. -
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omegaml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
omega|ml is the innovative Python-native MLOps platform that provides a scalable development and runtime environment for your Data Products. Works from laptop to cloud. -
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ChatGPT-Telegram-Workers
Deploy your own Telegram ChatGPT bot on Cloudflare Workers with ease
The simplest and fastest way to deploy your own ChatGPT Telegram bot. Use Cloudflare Workers, single file, copy and paste directly, no dependencies required, no need to configure local development environment, no domain name required, serverless. You can customize the system initialization information so that your debugged personality never disappears. -
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Deta Surf
Personal AI Notebooks. Organize files & webpages and generate notes
Surf is an open-source AI-driven development tool designed to simplify the process of building and experimenting with artificial intelligence applications. The platform provides a streamlined development environment where developers can test models, run experiments, and deploy small AI services with minimal infrastructure overhead. It focuses on simplicity and speed, allowing developers to prototype ideas quickly without managing complex cloud configurations. Surf integrates modern AI workflows such as prompt-based applications, lightweight APIs, and automated deployment pipelines. The platform is particularly useful for developers who want to experiment with AI models locally while maintaining the option to deploy them in production environments later. ... -
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Node.js Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models
...NLP Cloud serves high-performance pre-trained or custom models for NER, sentiment analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, text generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, and served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. -
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FastGPT
FastGPT is a knowledge-based platform built on the LLMs
FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily develop and deploy complex question-answering systems without the need for extensive setup or configuration. -
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Telegram Drive
Telegram Drive
Telegram Drive is a powerful utility that enables you to organize your telegram files and much more. Teldrive stands out among similar tools, thanks to its implementation in Go, a language known for its efficiency. Its performance surpasses alternatives written in Python and other languages, with the exception of Rust. Teldrive not only excels in speed but also offers an intuitive user interface for efficient file interaction which other tool lacks. Its compatibility with Rclone further... -
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Pathway AI Pipelines
Ready-to-run cloud templates for RAG
...The templates include built-in indexing, vector search, hybrid search, and caching capabilities that remove the need to assemble separate infrastructure components. Developers can run the applications locally or deploy them to cloud platforms using Docker with minimal setup. Overall, llm-app functions as a practical accelerator for teams building real-time, production-ready AI knowledge systems. -
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PHP Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. Pass the model you want to use and the NLP Cloud token to the client during initialization. If you are making asynchronous requests, you will always receive a quick response containing a URL.
