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.

A toolbox for deep learning model deployment using C++ YoloX | YoloV7 | YoloV8 | Gan | OCR | MobileVit | Scrfd | MobileSAM | StableDiffusion

The Doc Intelligence in-a-Box project leverages Azure AI Document Intelligence to extract data from PDF forms and store the data in a Azure Cosmos DB. This solution, part of the AI-in-a-Box framework by Microsoft Customer Engineers and Architects, ensures quality, efficiency, and rapid deployment of AI and ML solutions across various industries.

Bootable Kali Linux USB with OpenClaw AI automation. Portable penetration testing rig with pre-configured workflows, auto-documentation, and drop-box node deployment. Boot anywhere, pentest everything, leave no trace.

The AI Video Intelligence Solution Accelerator enables developers to deploy an end-to-end IoT Edge, including Azure Data Box Edge, based solution that processes camera feeds using CPU, GPU, and FPGA Azure Machine Learning accelerated models.

Hashtag 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.

A modular,application template for building, developing, and deploying an AI-powered applications with multi-agent orchestration, modern web frontends, and robust infrastructure-as-code to dynamically talk to your documents across different providers such as Google Drive, Box, and your local computer.

A lightweight object detection AI using SSD MobileNet V3, trained on COCO dataset for 80 classes. Deployed on Raspberry Pi 4B (4GB) with Rev 1.3 webcam for real-time inference. Displays bounding boxes, labels, and scores on video feed. Perfect for IoT projects like smart cameras. FPS: 5-10. Built with TensorFlow and OpenCV.

In this project, you'll develop a business proposal for an AI product. There are several important aspects of product development you'll need to think about and describe, and they are exactly the topics we've covered in this course. Here, we'll walk through what should be included in the proposal. You'll also find instructions in the Capsone Project Starter File, linked at the bottom of the page. You need to answer the questions in this file to complete the project. This project is open-ended in that you can propose any product in any industry that you want! If you're stuck, feel free to think about and research one of the use cases we've already discussed in this course. As a tip, you may find that your strongest ideas come from business arenas you are interested in and may already know a lot about. The Business Goal You'll need to describe what the product is, and how it will provide value to the business. It's important in this section to describe exactly what the product will do and why/how this helps the business. Focus on linking the AI/ML task to business goals such as increasing revenue or customer happiness. Success Metrics You'll also need to describe how you'll know whether the product is successful. Think about measurable, predictive, comparable, and benchmarked metrics for business success. Data You should carefully consider how you will acquire the data to train your model, and issues that may arise during data collection. Important considerations include: buying data vs. collecting it, personally identifying information (PII) and data sensitivity, cost, and whether data will be continuously available or acquired in one large batch (and need to be refreshed). Model When thinking about how you will build the model, will you use an in-house data science team because none of the out of the box platforms have your use case, or because you want the ability to control a particular aspect of your model? Frequently, when you use an external platform to build and host the model, their terms of service will require you to give them access to your data. Will this be an issue? Minimum Viable Product (MVP) You'll want to think about what your product is. What does it look like? Who uses it, and how do they use it? How will you actually build it? Post-MVP-Deployment Finally, what happens after launch? How do you ensure your product continues to perform well? Who will monitor it, how will they monitor it, and how often?

Deploying and managing AI agents on isolated boxes

AI Anime Generator is an open-source template for building your own AI-powered anime image generation website. It features modern tech, high-quality image generation, and out-of-the-box support for authentication, payment, and deployment—helping you launch your creative platform in minutes.

ai.deploy.box webassembly demo

A modern FastAPI template for building AI-powered chat applications with GraphAI. Features streaming responses, Docker deployment, and OpenTelemetry observability out of the box.

CS 1.6 server in a box. One command to deploy, AI to control. Quake sounds, bots, competitive config.

This project has deploied PGNet on Seeed Studio's AI Box.

Use streamlit to deploy some models in a black box direction (Week 4 module 1: Python toward AI DS)

A viral, SEO‑optimized full‑stack starter kit for AI‑driven content platforms – backend, frontend, and deployment ready out of the box.

vaiGO means Vitis-ai GO. We provide utility and tutorial that make it easy to convert a trained AI model into a bitstream that can be deployed on an FPGA Edge AI Box.

# AI Image Analysis API Production-ready object detection API using YOLOv8 + FastAPI ## Features - Object detection with bounding boxes - JSON responses - Deployed on Vercel

⚡ Full-stack YOLOv5 Object Detection System powered by FastAPI and React. Detect objects in images or live camera streams with real-time bounding boxes, labels, and confidence scores. Modular, production-ready design with clean UI, REST APIs, and easy deployment for AI-driven applications.

⚡ Full-stack YOLOv5 Object Detection System powered by FastAPI and React. Detect objects in images or live camera streams with real-time bounding boxes, labels, and confidence scores. Modular, production-ready design with clean UI, REST APIs, and easy deployment for AI-driven applications.

AI platform is a sellable, auto-deployable business-in-a-box

Out-of-the-box guide for voximplant-grok AI integration deployment

AI platform is a sellable, auto-deployable business-in-a-box

A GitHub template repository for deploying AI in regulated industries — governance docs, CI/CD gates, risk taxonomy, and release readiness out of the box

Developed and deployed an AI-powered box detection system achieving 90%+ accuracy for warehouse conveyor belt monitoring. Built with YOLOv8 and FastAPI, containerized with Docker for seamless deployment, enabling automated inventory tracking and quality control.

A self-hosted MCP (Model Context Protocol) solution that enables AI assistants to deploy and manage your static websites directly. Simple setup, ready to use out of the box.

A self-hosted MCP (Model Context Protocol) solution that enables AI assistants to deploy and manage your static websites directly. Simple setup, ready to use out of the box.

A self-hosted MCP (Model Context Protocol) solution that enables AI assistants to deploy and manage your static websites directly. Simple setup, ready to use out of the box.

A self-hosted MCP (Model Context Protocol) solution that enables AI assistants to deploy and manage your static websites directly. Simple setup, ready to use out of the box.