README.md

AI-in-a-Box

AI-in-a-Box leverages the collective expertise of Microsoft Customer Engineers and Architects across the globe to develop and provide AI and ML solutions to the technical community.

Our intent is to present 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.

As we continue to learn from the market, the contributors will look to equip the community with the tools and resources necessary to succeed in the ever-evolving AI and ML landscape.

Why AI-in-a-Box?

• Accelerated Deployment: Speed up your solutions with our proven, ready-to-use patterns.
• Cost Savings: Maximize your budget by reusing existing code and patterns.
• Enhanced Quality & Reliability: Trust in our solutions, validated through real-world scenarios.
• Competitive Advantage: Outpace competitors by accelerating solution deployment.

Available Guidance

Topic	Description
Responsible AI	This provides essential guidance on the responsible use of AI and LLM technologies.
Security for Generative AI Applications	This document provides specific security guidance for Generative AI (GenAI) applications.
Scaling OpenAI Applications	This document contains best practices for scaling OpenAI applications within Azure.

Available “-in-a-Box” accelerators

Pattern	Description	Supported Use Cases and Features
Azure ML Operationalization in-a-box	Boilerplate Data Science project from model development to deployment and monitoring	End-to-end MLOps project template Outer Loop (infrastructure setup) Inner Loop (model creation and deployment lifecycle)
Edge AI in-a-box	Edge AI from model creation to deployment on Edge Device(s)	Create a model and deploy to Edge Device. Outer Loop Infrastructure Setup (IoT Hub, IoT Edge, Edge VM, Container Registry, Azure ML) Inner Loop (model creation and deployment)
AML Edge in-a-box	Edge AI from model creation to deployment on Edge Device(s)	Orchestrate the entire Edge AI model lifecycle—from creation to deployment—using Azure ML, IoT Edge, and IoT Hub, while leveraging Azure ML CLI V2 for streamlined management.
Custom Vision Edge in-a-box	Edge AI from model creation to deployment on Edge Device(s)	Edge AI mitigates cloud latency by shifting analysis closer to the data source for faster responses. This accelerator demonstrates using Custom Vision to train a model and export it in formats like ONNX or Dockerfile for edge deployment.
Doc Intelligence in-a-box	This accelerator enables companies to automate PDF form processing, modernize operations, save time, and cut costs as part of their digital transformation journey.	Receive PDF Forms Function App and Logic App for Orchestration Document Intelligence Model creation for form processing and content extraction Saves PDF data in Azure Cosmos DB
Image and Video Analysis in-a-box	Extracts information from images and videos with Azure AI Vision and sends the results along with the prompt and system message to Azure GPT-4 Turbo with Vision.	Orchestration through Azure Data Factory Low code solution, easily extensible for your own use cases through ADF parameters Reuse same solution and deployed resources for many different scenarios Saves GPT4-V results to Azure CosmosDB
Cognitive Services Landing Zone in-a-box	Minimal enterprise-ready networking and AI Services setup to support most Cognitive Services scenarios in a secure environment	Hub-and-Spoke Vnet setup and peering Cognitive Service deployment Private Endpoint setup Private DNS integration with PaaS DNS resolver
Semantic Kernel Bot in-a-box	Extendable solution accelerator for advanced Azure OpenAI Bots	Deploy Azure OpenAI bot to multiple channels (Web, Teams, Slack, etc) Built-in Retrieval-Augmented Generation (RAG) support Implement custom AI Plugins
NLP to SQL in-a-box	Unleash the power of a cutting-edge speech-enabled SQL query system with Azure OpenAI, Semantic Kernel, and Azure Speech Services. Simply speak your data requests in natural language, and let the magic happen.	Allows users to verbally express natural language queries Translate into SQL statements using Azure Speech & AOAI Execute on an Azure SQL DB
Assistants API notebooks	Harnessing the simplicity of the Assistants API, developers can seamlessly integrate assistants with diverse functionalities, from executing code to retrieving data, empowering users with versatile and dynamic digital assistants tailored to their needs.	Offers three main capabilities: Code Interpreter (tech tasks), Retrieval (finding info), and Function calling (task execution) These powers combine to form a versatile super-assistant for handling diverse tasks
Assistants API Bot in-a-box	This sample provides a step-by-step guide on how to deploy a virtual assistant leveraging the Azure OpenAI Assistants API. It covers the infrastructure deployment, configuration on the AI Studio and Azure Portal, and end-to-end testing examples.	Deploy the necessary infrastructure to support an Azure OpenAI Assistant Configure as Assistant with the required tools. Connect a Bot Framework application to your Assistant to deploy the chat to multiple channels

Key Contacts & Contributors

If you have any questions or would like to contribute please reach out to: [email protected]

Contact	GitHub ID	Email
Alex Morales	@msalemor	[email protected]
Andre Dewes	@andredewes	[email protected]
Andrés Padilla	@AndresPad	[email protected]
Chris Ayers	@codebytes	[email protected]
Eduardo Noriega	@EduardoN	[email protected]
Franklin Guimaraes	@franklinlindemberg	[email protected]
Jean Hayes	@jehayesms	[email protected]
Marco Aurélio Bigélli Cardoso	@MarcoABCardoso	[email protected]
Maria Vrabie	@MariaVrabie	[email protected]
Neeraj Jhaveri	@neerajjhaveri	[email protected]
Thiago Rotta	@rottathiago	[email protected]
Victor Santana	@Welasco	[email protected]
Sabyasachi Samaddar	@ssamadda	[email protected]