/AI-in-a-Box

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AI-in-a-Box

FTA AI-in-a-Box: Deployment Accelerator

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.


FTA AI-in-a-Box: Deployment Accelerator

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)
  • 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 in-a-box 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: aibox@microsoft.com

    Contact GitHub ID Email
    Alex Morales @msalemor alemor@microsoft.com
    Andre Dewes @andredewes andredewes@microsoft.com
    Andrés Padilla @AndresPad andres.padilla@microsoft.com
    Chris Ayers @codebytes chrisayers@microsoft.com
    Eduardo Noriega @EduardoN ednorieg@microsoft.com
    Jean Hayes @jehayesms jean.hayes@microsoft.com
    Marco Aurélio Bigélli Cardoso @MarcoABCardoso macardoso@microsoft.com
    Maria Vrabie @MariaVrabie mavrabie@microsoft.com
    Neeraj Jhaveri @neerajjhaveri neeraj.jhaveri@microsoft.com
    Thiago Rotta @rottathiago thiago.rotta@microsoft.com
    Victor Santana @Welasco vsantana@microsoft.com
    Sabyasachi Samaddar @ssamadda ssamadda@microsoft.com