Best AI tools for< Deploy Environments >

VEGA AI is an AI-powered virtual entity designed for guidance and assistance in the field of education. It offers an AI-first learning platform that allows users to effortlessly create personalized courses, gain learning insights, and support students with the help of an AI avatar. Trusted by leading institutions worldwide, VEGA AI helps educators deploy adaptive learning environments, build personalized learning paths, and provide 24/7 AI avatar support. The platform aims to transform teaching by automating workflows, reducing workload, and boosting student outcomes through AI automation and personalized learning journeys.

TitanML is a platform that provides tools and services for deploying and scaling Generative AI applications. Their flagship product, the Titan Takeoff Inference Server, helps machine learning engineers build, deploy, and run Generative AI models in secure environments. TitanML's platform is designed to make it easy for businesses to adopt and use Generative AI, without having to worry about the underlying infrastructure. With TitanML, businesses can focus on building great products and solving real business problems.

Qubinets is a cloud data environment solutions platform that provides building blocks for building big data, AI, web, and mobile environments. It is an open-source, no lock-in, secured, and private platform that can be used on any cloud, including AWS, Digital Ocean, Google Cloud, and Microsoft Azure. Qubinets makes it easy to plan, build, and run data environments, and it streamlines and saves time and money by reducing the grunt work in setup and provisioning.

VeroCloud is a platform offering tailored solutions for AI, HPC, and scalable growth. It provides cost-effective cloud solutions with guaranteed uptime, performance efficiency, and cost-saving models. Users can deploy HPC workloads seamlessly, configure environments as needed, and access optimized environments for GPU Cloud, HPC Compute, and Tally on Cloud. VeroCloud supports globally distributed endpoints, public and private image repos, and deployment of containers on secure cloud. The platform also allows users to create and customize templates for seamless deployment across computing resources.

GenWorlds is an event-based communication framework for building multi-agent systems. It offers a platform for creating Generative AI applications where users can design customizable environments, utilize scalable architecture, access a repository of memories and tools, choose cognitive processes for agents, and pick coordination protocols. GenWorlds aims to foster a vibrant community of developers, AI enthusiasts, and innovators to collaborate, innovate, share knowledge, and grow together.

LuckyRobots is an AI tool designed to make robotics accessible to software engineers by providing a simulation platform for deploying end-to-end AI models. The platform allows users to interact with robots using natural language commands, explore virtual environments, test robot models in realistic scenarios, and receive camera feeds for monitoring. LuckyRobots aims to train AI models on real-world simulations and respond to natural language inputs, offering a user-friendly and innovative approach to robotics development.

Scaled Foundations is an AI application that offers a web-based platform for developing, training, validating, and deploying intelligent robots faster. It provides both Open GRID, a platform for general robot intelligence development, and GRID Enterprise, a packaged version for a scalable, customizable, and private robot training experience. The application aims to democratize advanced robot AI development by integrating technologies like Isaac Sim and AirSim, enabling users to accelerate their research and build robotic enterprises more efficiently.

Release.ai is an AI-centric platform that allows developers, operations, and leadership teams to easily deploy and manage AI applications. It offers pre-configured templates for popular open-source technologies, private AI environments for secure development, and access to GPU resources. With Release.ai, users can build, test, and scale AI solutions quickly and efficiently within their own boundaries.

Prodvana is an intelligent deployment platform that helps businesses automate and streamline their software deployment process. It provides a variety of features to help businesses improve the speed, reliability, and security of their deployments. Prodvana is a cloud-based platform that can be used with any type of infrastructure, including on-premises, hybrid, and multi-cloud environments. It is also compatible with a wide range of DevOps tools and technologies. Prodvana's key features include: Intent-based deployments: Prodvana uses intent-based deployment technology to automate the deployment process. This means that businesses can simply specify their deployment goals, and Prodvana will automatically generate and execute the necessary steps to achieve those goals. This can save businesses a significant amount of time and effort. Guardrails for deployments: Prodvana provides a variety of guardrails to help businesses ensure the security and reliability of their deployments. These guardrails include approvals, database validations, automatic deployment validation, and simple interfaces to add custom guardrails. This helps businesses to prevent errors and reduce the risk of outages. Frictionless DevEx: Prodvana provides a frictionless developer experience by tracking commits through the infrastructure, ensuring complete visibility beyond just Docker images. This helps developers to quickly identify and resolve issues, and it also makes it easier to collaborate with other team members. Intelligence with Clairvoyance: Prodvana's Clairvoyance feature provides businesses with insights into the impact of their deployments before they are executed. This helps businesses to make more informed decisions about their deployments and to avoid potential problems. Easy integrations: Prodvana integrates seamlessly with a variety of DevOps tools and technologies. This makes it easy for businesses to use Prodvana with their existing workflows and processes.

H2O.ai is a leading AI platform that offers a range of open-source and enterprise solutions for machine learning and AI applications. The platform includes products such as H2O-3, H2O Wave, Sparkling Water, H2O AI Cloud, H2O Driverless AI, and more. H2O.ai aims to democratize AI by providing tools for building, deploying, and managing AI/ML models in various environments, including the cloud. The platform also emphasizes explainable AI to enhance transparency and trustworthiness in AI applications.

Vairflow is an AI-driven Integrated Development Environment (IDE) that empowers developers to build faster and more efficiently. It simplifies complex ideas into components, allowing seamless development and deployment of backend microservices, web UI, and mobile app UI. With upcoming AI features like code generation, completion, and explanation, Vairflow aims to enhance productivity and streamline the development process. The platform also offers flexible deployment options, cost-effective usage, and seamless collaboration, ensuring no vendor lock-in and easy context switching between projects and environments.

GrapixAI is a leading provider of low-cost cloud GPU rental services and AI server solutions. The company's focus on flexibility, scalability, and cutting-edge technology enables a variety of AI applications in both local and cloud environments. GrapixAI offers the lowest prices for on-demand GPUs such as RTX4090, RTX 3090, RTX A6000, RTX A5000, and A40. The platform provides Docker-based container ecosystem for quick software setup, powerful GPU search console, customizable pricing options, various security levels, GUI and CLI interfaces, real-time bidding system, and personalized customer support.

crewAI is a platform for Multi AI Agents Systems that offers a user-friendly framework for automating workflows with AI agents. It simplifies the process of building and deploying multi-agent automations, providing support for various AI models and templates. With a focus on privacy and security, crewAI ensures that each agent runs in isolated environments. The platform is suitable for enterprises and developers looking to leverage AI technologies effectively.

Lamini is an enterprise-level LLM platform that offers precise recall with Memory Tuning, enabling teams to achieve over 95% accuracy even with large amounts of specific data. It guarantees JSON output and delivers massive throughput for inference. Lamini is designed to be deployed anywhere, including air-gapped environments, and supports training and inference on Nvidia or AMD GPUs. The platform is known for its factual LLMs and reengineered decoder that ensures 100% schema accuracy in the JSON output.

BackX is an AI development platform that empowers developers to quickly ship backends across various use cases, environments, and scales. It offers unparalleled accuracy, flexibility, and efficiency by overcoming the limitations of traditional AI-assisted programming. With features like one-click production-grade code, context-aware consistent code output, versioned artifacts, instant deploy, and a suite of AI-powered dev tools, BackX revolutionizes the backend development process. Developers can effortlessly design and manage databases, generate CRUD operations, implement complex business logic, and deploy serverless applications with ease. The platform aims to streamline development processes, increase cost-effectiveness, and provide more accurate outputs than traditional methods.

Luxonis is an AI application that offers Visual AI solutions engineered for precision edge inference. The application provides stereo depth cameras with unique features and quality, enabling users to perform advanced vision tasks on-device, reducing latency and bandwidth demands. With open-source DepthAI API, users can create and deploy custom vision solutions that scale with their needs. Luxonis also offers real-world training data for self-improving vision intelligence and operates flawlessly through vibrations, temperature shifts, and extended use. The application integrates advanced sensing capabilities with up to 48MP cameras, wide field of view, IMUs, microphones, ToF, thermal, IR illumination, and active stereo for unparalleled perception.

Ongil is an AI solutions provider that helps industries transform their expertise into AI solutions in a matter of weeks. By leveraging domain-optimized components, Ongil accelerates AI development by 10x, enabling businesses to deploy production-ready AI solutions. The platform offers tailored AI solutions for various industry challenges, personalized portfolio optimization, emissions estimation, supply chain intelligence, and more. Ongil's unique approach combines industry-specific components to create maintainable and scalable AI systems for enterprise production environments.

Deploya is an AI-powered platform that allows users to create production-ready websites in seconds. By leveraging cutting-edge AI models, Deploya optimizes websites for performance and user experience. Users can simply describe their requirements, and Deploya will generate a website accordingly. The platform also offers features like automatic image selection, quick publishing, and flexible pricing options. Deploya stands out for its AI-driven web design capabilities and efficient website deployment process.

MagikKraft is an AI-powered platform that simplifies complex controls by enabling users to create personalized sequences and actions for programmable devices like drones, automated appliances, and self-driving vehicles. Users can craft, simulate, and deploy customized recipes through the AI-powered tool, enhancing the potential of technology while prioritizing privacy, user control, and creative freedom.

Amazon Bedrock is a cloud-based platform that enables developers to build, deploy, and manage serverless applications. It provides a fully managed environment that takes care of the infrastructure and operations, so developers can focus on writing code. Bedrock also offers a variety of tools and services to help developers build and deploy their applications, including a code editor, a debugger, and a deployment pipeline.

LLMariner is an extensible open source platform built on Kubernetes to simplify the management of generative AI workloads. It enables efficient handling of training and inference data within clusters, with OpenAI-compatible APIs for seamless integration with a wide range of AI-driven applications.

This repository is a sample chatbot using the Anthropic company's LLM Claude, one of the foundational models provided by Amazon Bedrock for generative AI. It allows users to have basic conversations with the chatbot, personalize it with their own instructions and external knowledge, and analyze usage for each user/bot on the administrator dashboard. The chatbot supports various languages, including English, Japanese, Korean, Chinese, French, German, and Spanish. Deployment is straightforward and can be done via the command line or by using AWS CDK. The architecture is built on AWS managed services, eliminating the need for infrastructure management and ensuring scalability, reliability, and security.

BentoML is an open-source model serving library for building performant and scalable AI applications with Python. It comes with everything you need for serving optimization, model packaging, and production deployment.

LLM-TPU project aims to deploy various open-source generative AI models on the BM1684X chip, with a focus on LLM. Models are converted to bmodel using TPU-MLIR compiler and deployed to PCIe or SoC environments using C++ code. The project has deployed various open-source models such as Baichuan2-7B, ChatGLM3-6B, CodeFuse-7B, DeepSeek-6.7B, Falcon-40B, Phi-3-mini-4k, Qwen-7B, Qwen-14B, Qwen-72B, Qwen1.5-0.5B, Qwen1.5-1.8B, Llama2-7B, Llama2-13B, LWM-Text-Chat, Mistral-7B-Instruct, Stable Diffusion, Stable Diffusion XL, WizardCoder-15B, Yi-6B-chat, Yi-34B-chat. Detailed model deployment information can be found in the 'models' subdirectory of the project. For demonstrations, users can follow the 'Quick Start' section. For inquiries about the chip, users can contact SOPHGO via the official website.

Tau is a framework for building low maintenance & highly scalable cloud computing platforms that software developers will love. It aims to solve the high cost and time required to build, deploy, and scale software by providing a developer-friendly platform that offers autonomy and flexibility. Tau simplifies the process of building and maintaining a cloud computing platform, enabling developers to achieve 'Local Coding Equals Global Production' effortlessly. With features like auto-discovery, content-addressing, and support for WebAssembly, Tau empowers users to create serverless computing environments, host frontends, manage databases, and more. The platform also supports E2E testing and can be extended using a plugin system called orbit.

AingDesk is a tool that allows users to deploy DeepSeek or other AI models on their computer with just one click. It features a user-friendly interface, multi-source knowledge base support, built-in chat interface, and the ability to share projects online. The tool is optimized for performance on both local and cloud environments, with a focus on hassle-free setup and extensibility through a modular architecture. The development plan includes support for third-party API integrations and local deployment of text-to-image hybrid models for creative workflows.

llama_deploy is an async-first framework for deploying, scaling, and productionizing agentic multi-service systems based on workflows from llama_index. It allows building workflows in llama_index and deploying them seamlessly with minimal changes to code. The system includes services endlessly processing tasks, a control plane managing state and services, an orchestrator deciding task handling, and fault tolerance mechanisms. It is designed for high-concurrency scenarios, enabling real-time and high-throughput applications.

Flyte is an open-source orchestrator that facilitates building production-grade data and ML pipelines. It is built for scalability and reproducibility, leveraging Kubernetes as its underlying platform. With Flyte, user teams can construct pipelines using the Python SDK, and seamlessly deploy them on both cloud and on-premises environments, enabling distributed processing and efficient resource utilization.

Build your dream team with Autogen is a repository that leverages Microsoft Autogen 0.4, Azure OpenAI, and Streamlit to create an end-to-end multi-agent application. It provides an advanced multi-agent framework based on Magentic One, with features such as a friendly UI, single-line deployment, secure code execution, managed identities, and observability & debugging tools. Users can deploy Azure resources and the app with simple commands, work locally with virtual environments, install dependencies, update configurations, and run the application. The repository also offers resources for learning more about building applications with Autogen.

Trigger.dev is an open source platform and SDK for creating long-running background jobs. It provides features like JavaScript and TypeScript SDK, no timeouts, retries, queues, schedules, observability, React hooks, Realtime API, custom alerts, elastic scaling, and works with existing tech stack. Users can create tasks in their codebase, deploy tasks using the SDK, manage tasks in different environments, and have full visibility of job runs. The platform offers a trace view of every task run for detailed monitoring. Getting started is easy with account creation, project setup, and onboarding instructions. Self-hosting and development guides are available for users interested in contributing or hosting Trigger.dev.

Netsaur is a powerful machine learning library for Deno, offering a lightweight and easy-to-use neural network solution. It is blazingly fast and efficient, providing a simple API for creating and training neural networks. Netsaur can run on both CPU and GPU, making it suitable for serverless environments. With Netsaur, users can quickly build and deploy machine learning models for various applications with minimal dependencies. This library is perfect for both beginners and experienced machine learning practitioners.

Tiledesk is an Open Source Live Chat platform with integrated Chatbots written in NodeJs and Express. It provides a multi-channel platform for Web, Android, and iOS, offering out-of-the-box chatbots that work alongside humans. Users can automate conversations using native chatbot technology powered by AI, connect applications via APIs or Webhooks, deploy visual applications within conversations, and enable applications to interact with chatbots or end-users. Tiledesk is multichannel, allowing chatbot scripts with images and buttons to run on various channels like Whatsapp, Facebook Messenger, and Telegram. The project includes Tiledesk Server, Dashboard, Design Studio, Chat21 ionic, Web Widget, Server, Http Server, MongoDB, and a proxy. It offers Helm charts for Kubernetes deployment, but customization is recommended for production environments, such as integrating with external MongoDB or monitoring/logging tools. Enterprise customers can request private Docker images by contacting [email protected].

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

AI-in-a-Box is a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency. It provides essential guidance on the responsible use of AI and LLM technologies, specific security guidance for Generative AI (GenAI) applications, and best practices for scaling OpenAI applications within Azure. The available accelerators include: Azure ML Operationalization in-a-box, Edge AI in-a-box, Doc Intelligence in-a-box, Image and Video Analysis in-a-box, Cognitive Services Landing Zone in-a-box, Semantic Kernel Bot in-a-box, NLP to SQL in-a-box, Assistants API in-a-box, and Assistants API Bot in-a-box.

Cluster Toolkit is an open-source software by Google Cloud for deploying AI/ML and HPC environments on Google Cloud. It allows easy deployment following best practices, with high customization and extensibility. The toolkit includes tutorials, examples, and documentation for various modules designed for AI/ML and HPC use cases.

Ollama Operator is a Kubernetes operator designed to facilitate running large language models on Kubernetes clusters. It simplifies the process of deploying and managing multiple models on the same cluster, providing an easy-to-use interface for users. With support for various Kubernetes environments and seamless integration with Ollama models, APIs, and CLI, Ollama Operator streamlines the deployment and management of language models. By leveraging the capabilities of lama.cpp, Ollama Operator eliminates the need to worry about Python environments and CUDA drivers, making it a reliable tool for running large language models on Kubernetes.

ONNX Runtime Server is a server that provides TCP and HTTP/HTTPS REST APIs for ONNX inference. It aims to offer simple, high-performance ML inference and a good developer experience. Users can provide inference APIs for ONNX models without writing additional code by placing the models in the directory structure. Each session can choose between CPU or CUDA, analyze input/output, and provide Swagger API documentation for easy testing. Ready-to-run Docker images are available, making it convenient to deploy the server.

The Oracle Cloud Infrastructure Data Science and AI services Examples repository provides demos, tutorials, and code examples showcasing various features of the OCI Data Science service and AI services. It offers tools for data scientists to develop and deploy machine learning models efficiently, with features like Accelerated Data Science SDK, distributed training, batch processing, and machine learning pipelines. Whether you're a beginner or an experienced practitioner, OCI Data Science Services provide the resources needed to build, train, and deploy models easily.

This repository provides an easy-to-use solution for running inference servers on Slurm-managed computing clusters using vLLM. All scripts in this repository run natively on the Vector Institute cluster environment. Users can deploy models as Slurm jobs, check server status and performance metrics, and shut down models. The repository also supports launching custom models with specific configurations. Additionally, users can send inference requests and set up an SSH tunnel to run inference from a local device.

This repository provides Terraform configurations to automatically set up Google Cloud resources and deploy Dify in a highly available configuration. It includes features such as serverless hosting, auto-scaling, and data persistence. Users need a Google Cloud account, Terraform, and gcloud CLI installed to use this tool. The configuration involves setting environment-specific values and creating a GCS bucket for managing Terraform state. The tool allows users to initialize Terraform, create Artifact Registry repository, build and push container images, plan and apply Terraform changes, and cleanup resources when needed.

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