├── .gitignore ├── CODE_OF_CONDUCT.md ├── LICENSE ├── OpenAIDemos └── README.md ├── README.md ├── SECURITY.md ├── SUPPORT.md └── imgs ├── acc.jpg └── ai.png /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | Asset publishing framework.pptx 131 | .gitignore 132 | Solution Accelerators for the Azure AI Platform.pptx 133 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Microsoft Open Source Code of Conduct 2 | 3 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 4 | 5 | Resources: 6 | 7 | - [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) 8 | - [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) 9 | - Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns 10 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) Microsoft Corporation. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE 22 | -------------------------------------------------------------------------------- /OpenAIDemos/README.md: -------------------------------------------------------------------------------- 1 | # Azure OpenAI Accelerators and Demo Assets 2 | 3 | The following is a list of Repeatable IP and learning resources to quickly build an initial Azure OpenAI solution, developed by different individuals and teams at Microsoft. 4 | 5 | ## Accelerators and Workshops 6 | 7 | ### Official Azure OpenAI Accelerators 8 | 9 | Below is a summary list of the official Azure OpenAI Accelerators and workshops: 10 |
11 | 12 | 13 | | Name | Description | Link | 14 | | ----------- | ----------- | ----------- | 15 | | Azure OpenAI in a Day workshop | This technical workshop will provide an introduction to OpenAI and an overview of Azure OpenAI Studio. Participants will be prompted to complete engineering exercises and use OpenAI to access company data. They will also learn about embedding solution accelerators and prototyping one use case from start to finish.| [Link](https://github.com/microsoft/azure-openai-in-a-day-workshop) 16 | | Azure OpenAI Workshop | In this workshop, you will learn how to use the Azure OpenAI service to create AI powered solutions. You will get hands-on experience with the latest AI technologies and will learn how to use Azure OpenAI API. | [Link](https://github.com/Azure/azure-openai-workshop) 17 | | Business Process Automation solution accelerator | Creates pipelines to analyze text and audio datasets, across multiple cognitive services, and the HuggingFace library. The accelerator deploys all of the resources, and transforms the input data at each step, allowing multiple Cognitive Services to be called and deployed within a single, end-to-end pipeline. Includes capabilities like Azure OpenAI (summarization or custom prompts) and integration with CosmosDb, Cognitive Search, and RediSearch for Vector Search | [Link](https://github.com/Azure/business-process-automation) 18 | |ChatGPT + Enterprise data with Azure OpenAI and Cognitive Search | This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure Cognitive Search for data indexing and retrieval. **NOTE: sample created by Product Group**| [Link](https://github.com/Azure-Samples/azure-search-openai-demo/) 19 | |Knowledge Mining with Azure OpenAI| The purpose of this repo is to accelerate the deployment of a Python-based Knowledge Mining solution with OpenAI that will ingest a Knowledge Base, generate embeddings using the contents extracted, store them in a vector search engine (Redis), and use that engine to answer queries / questions specific to that Knowledge Base. The repo includes the use of MRKL/ReAct Prompts and the ability to ask questions of different stores (including mathematical operations). | [Link](https://github.com/MSUSAzureAccelerators/Knowledge-Mining-with-OpenAI) 20 | | OpenAI workshop | Workshop materials to build intelligent solutions on Open AI | [Link](https://github.com/microsoft/OpenAIWorkshop) 21 | |Semantic Kernel |Semantic Kernel (SK) is a lightweight SDK enabling integration of AI Large Language Models (LLMs) with conventional programming languages.| [Link](https://github.com/microsoft/semantic-kernel) 22 | |Visual ChatGPT |Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. | [Link](https://github.com/microsoft/visual-chatgpt) 23 | 24 |
25 | 26 | ### Microsoft Community Contributions 27 | Below is a summary list of Azure OpenAI Microsoft Community Contributions: 28 |
29 | 30 | | Name | Description | Link | 31 | | ----------- | ----------- | ----------- | 32 | | Athena - Generate Synapse queries with Azure OpenAI | We know that LLMs can generate SQL code from Natural language. The challenge in adopting this to empower all skill levels to query big data is many fold. From LLM perspective: For a correct SQL query generation from natural langugae, LLMs not only need to understad the context but also have an understanding of the database schema. Passing schema to prompts could be an approach here but this is not scalable. In this repo we showcase using prompt engineering approaches from chain of thought modelling we can make this approach scalable. This project shows LLMs working from natural language to query a star schema in data lake (via Synapse) without the need to know the DB schema before hand. | [Link](https://github.com/Ritaja/Athena) 33 | | Azure OpenAI Embeddings QnA | A simple web application for a OpenAI-enabled document search. This repo uses Azure OpenAI Service for creating embeddings vectors from documents. For answering the question of a user, it retrieves the most relevant document and then uses GPT-3 to extract the matching answer for the question. | [Link](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) 34 | |Azure OpenAI Example Prompts |This repository shares example code and example prompts for accomplishing common tasks with the Azure OpenAI API.| [Link](https://github.com/jakeatmsft/AzureOpenAIExamples) 35 | |Azure OpenAI integration with Azure Cognitive-Search for document analysis | Azure OpenAI integration as a custom skillset in Azure Cognitive Search | [Link](https://github.com/Anaig/OpenAI-and-Cognitive-Search/) 36 | |Azure Cognitive Semantic Search with OpenAI enrichment |Azure Cognitive Semantic Search that works on large documents, with OpenAI enrichment. | [Link](https://github.com/MaheshSQL/cognitive-semantic-search-openai-accelerator) 37 | |ChatGPT with Enterprise Data |This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo and gpt3), and vector store (Pinecone, Redis and others) or Azure cognitive search for data indexing and retrieval. | [Link](https://github.com/akshata29/chatpdf) 38 | |Document Analysis using OpenAI GPT-3 |This repository provides a set of examples in Jupyter Notebooks/Python for performing document analysis using Azure OpenAI's GPT-3 | [Link](https://github.com/ryubidragonfire/document-analysis-using-gpt-3) 39 | |GPT-Azure-Search-Engine | This repo shows how you can use Azure OpenAI + Azure Cognitive Search to have a Smart and Multilingual Search engine that not only provides links of the search results, but also answers the question. | [Link](https://github.com/pablomarin/GPT-Azure-Search-Engine) 40 | | GPT3 Prompt Examples | GPT-3 examples using mostly text-davinci-003 | [Link](https://gist.github.com/csiebler/d137386c4a63cc34810151bcdf150d54) 41 | |Podcast Synopsis with OpenAI GPT|This repo illustrates how to use OpenAI GPT to generate a synopsis from a podcast transcription into multiple languages, generate 2-3 tag lines based on the podcast content and generate search engine optimised (SEO) keywords.|[Link](https://github.com/ryubidragonfire/generate-podcast-synopsis-OpenAI-GPT) 42 | | Simple Chatbot using Azure OpenAI service | Step-by-step article describing how to create a chatbot based on the Azure OpenAI text-davinci model.| [Link](https://github.com/michalmar/openai-demos-bot-webapp) 43 | 44 |
45 | 46 |
47 | 48 | **NOTE**: Please make sure to check the terms of use for each. Some are only provided for demonstration purposes and not mean for as-is production use. 49 | 50 | ## Learning Resources 51 | 52 | Below is a summary list of some learning resources: 53 |
54 | 55 | 56 | |Description | Link | 57 | |----------- | ----------- | 58 | | Azure OpenAI Documentation | [Link](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/) 59 | | OpenAI Cookbook | [Link](https://github.com/openai/openai-cookbook) 60 | 61 |
62 | 63 | **NOTE**: If you are a Microsoft employee and would like to add an asset to this list, please contact the Specialized AI CSA team. 64 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ![alt text](imgs/acc.jpg "Title") 4 | 5 |
6 |
7 | 8 | 9 | 10 | 11 | 12 | # AI Solution Accelerators 13 | 14 | Developed by the Microsoft AI Rangers Team, the AI Solution Accelerators are repeatable IP meant to provide developers with all the resources needed to quickly build an initial solution. The objective is to jump-start the development efforts and to learn the used technologies in as little time as possible. The AI Solution Accelerators should be considered as templates that are fully customizable to the user's unique business case. 15 | 16 | These accelerators can be deployed on the Azure platform. Visit the official [Azure AI webpage](https://azure.microsoft.com/en-us/overview/ai-platform/) to learn more about Azure AI solution offerings. 17 | 18 |
19 |
20 | 21 | ## Summary Table of AI Solution Accelerators 22 | Below is a summary list of the AI Solution Accelerators: 23 |
24 |
25 | 26 | | Name | Description | Services | Link | 27 | | ----------- | ----------- | ----------- | ----------- | 28 | | Many Models Solution Accelerator | For Machine Learning scenarios where having individual models performs better than a single larger model, and you need to train and maintain hundreds or thousands of models. | Azure Machine Learning | [Link](https://github.com/microsoft/solution-accelerator-many-models) 29 | | Data and Model Drift Detection | Ready to use solution to detect both data drift and model drift in Machine Learning/Data Science scenarios. | Azure Machine Learning | [Link](https://github.com/Azure/data-model-drift) 30 | | Knowledge Mining Solution Accelerator | All of the resources neeed to quickly build an initial Knowledge Mining solution with Azure Cognitive Search and Cognitive Services | Cognitive Search, Speech, Language, Translator, Computer Vision | [Link](https://docs.microsoft.com/en-us/samples/azure-samples/azure-search-knowledge-mining/azure-search-knowledge-mining/) 31 | | Business Process Automation | Creates pipelines to analyze text and audio datasets, across multiple cognitive services, and the HuggingFace library. The accelerator deploys all of the resources, and transforms the input data at each step, allowing multiple Cognitive Services to be called and deployed within a single, end-to-end pipeline. | Form Recognizer, Language, Speech, Computer Vision, AML, Blob storage, CosmosDB | [Link](https://github.com/Azure/business-process-automation) 32 | | Social Media Analytics | Social Media monitoring platform that helps collect data from social media sites and websites and evaluate that data to make business decisions. | Text Analytics, Translator, CosmosDB, Synapse, Power BI | [Link](https://github.com/microsoft/Azure-Social-Media-Analytics-Solution-Accelerator) 33 | | Medical Imaging | ML-based medical imaging asset using Azure that covers medical imaging use cases based on classification, object detection and instance segmentation. |AML including AutoML for Images | [Link](https://github.com/Azure/medical-imaging) 34 | | Retail Recommender | Creates an end-to-end solution for large retailers with an e-commerce channel to provide personalized product recommendations to users based on their purchase history, product selection in the e-commerce channel, and their activity in the physical store. | Cosmos DB, Synapse, ADLS, AML, AKS, PBI | [Link](https://github.com/microsoft/Azure-Synapse-Retail-Recommender-Solution-Accelerator) 35 | | MLOps v2 | MLOps v2 will allow AI professionals and our customers to deploy an end-to-end standardized and unified Machine Learning lifecycle scalable across multi workspaces. By abstracting agnostic infrastructure in an outer loop, the customer can focus on the inner loop development of their use cases. | Azure Machine Learning | [Link](https://microsoft.sharepoint.com/teams/CS_AzureDataAI/SitePages/Mlops.aspx) 36 | 37 | 38 | 39 | 40 |
41 |
42 | 43 | ## Use Cases behind the AI Solution Accelerators in More Details 44 | 45 | We provide more details below for each AI solution accelerator and its intended use: 46 |

47 | 48 | - [Many Models Solution Accelerator ](https://github.com/microsoft/solution-accelerator-many-models): In the real world, many problems can be too complex to be solved by a single machine learning model. Azure Machine Learning (AML) makes it easy to train, operate, and manage hundreds or even thousands of models. This repo will walk you through the end to end process of creating a many models solution from training to scoring to monitoring. 49 |

50 | - [Data and Model Drift Detection](https://github.com/Azure/data-model-drift): The environment of our world is constantly changing. For machine learning, this means that deployed models are confronted with unknown data and can become outdated over time. A proactive drift management approach is required to ensure that productive AI services deliver consistent business value in the long term. This accelerator will help create automated pipelines to identify data drift regularly as part of an MLOps architecture. 51 |

52 | - [Knowledge Mining Solution Accelerator](https://docs.microsoft.com/en-us/samples/azure-samples/azure-search-knowledge-mining/azure-search-knowledge-mining/): This accelerator provides all of the artifacts needed to quickly create a Cognitive Search Solution that includes templates for deploying the appropriate Azure resources, assets for creating the first search index, templates for using custom skills, a basic web app, and PowerBI reports to monitor search solution performance. Best practices were infused throughout the documentation to help guide the user. With Cognitive Search, the user can easily index both digital data (such as documents and text files) and analog data (such as images and scanned documents). 53 |

54 | - [Business Process Automation](https://github.com/Azure/business-process-automation): This accelerator provides a no code Studio for users to quickly build complex, multi-stage AI pipelines across multiple Azure AI and ML Services. Users can select, and stack, AI/ML Services from across Azure Cognitive Services (Speech, Language, Form Recognizer, ReadAPI), Azure Machine Learning, and even Hugging Face state-of-the-art models, into a single, fully integrated pipeline. Integration between services is automated by BPA, and once deployed, a web app is created. This customizable UI* provides and drag-n-drop interface for end users to build multi service pipelines. Finally, the user-created pipeline is triggered as soon as the first input file(s) are uploaded, storing the results in a CosmosDB. 55 |

56 | - [Social Media Analytics](https://github.com/microsoft/Azure-Social-Media-Analytics-Solution-Accelerator): The Social Media Accelerator provides the skeleton for building a Social Media monitoring platform that helps collect data from social media sites and websites and evaluate that data to make business decisions. This accelerator provides all the necessary resources to deploy the solution, as well as ideas and scenarios for extending the solution. 57 |

58 | - [Medical Imaging](https://github.com/Azure/medical-imaging): The purpose of this accelerator is to demonstrate how Azure Machine Learning can be used to support medical imaging and other use cases in areas like data and model management, deployment, experiment tracking and explainability. Furthermore, we cover various data science approaches ranging from manual model development with PyTorch to automated machine learning for images. Another focus is to provide MLOPS based examples for automating the machine learning lifecycle for medical use cases including retraining when new data becomes available. All use cases are based on publicly available datasets like brain RMI scans, cell micrographs, chest x-ray images and more. Since we cannot distribute the data directly, we refer to publicly available download locations. 59 |

60 | - [Retail Recommender](https://github.com/microsoft/Azure-Synapse-Retail-Recommender-Solution-Accelerator): This accelerator was built to provide developers with all of the resources needed to quickly build an Retail Recommender Solution based on Azure. 61 |

62 | - [MLOps v2](https://microsoft.sharepoint.com/teams/CS_AzureDataAI/SitePages/Mlops.aspx): This accelerator will allow AI professionals and our customers to deploy an end-to-end standardized and unified Machine Learning lifecycle scalable across multi workspaces. By abstracting agnostic infrastructure in an outer loop, the customer can focus on the inner loop development of their use cases. 63 |

64 |
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84 | -------------------------------------------------------------------------------- /SECURITY.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ## Security 4 | 5 | Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). 6 | 7 | If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/opensource/security/definition), please report it to us as described below. 8 | 9 | ## Reporting Security Issues 10 | 11 | **Please do not report security vulnerabilities through public GitHub issues.** 12 | 13 | Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report). 14 | 15 | If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/opensource/security/pgpkey). 16 | 17 | You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://aka.ms/opensource/security/msrc). 18 | 19 | Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: 20 | 21 | * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) 22 | * Full paths of source file(s) related to the manifestation of the issue 23 | * The location of the affected source code (tag/branch/commit or direct URL) 24 | * Any special configuration required to reproduce the issue 25 | * Step-by-step instructions to reproduce the issue 26 | * Proof-of-concept or exploit code (if possible) 27 | * Impact of the issue, including how an attacker might exploit the issue 28 | 29 | This information will help us triage your report more quickly. 30 | 31 | If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/opensource/security/bounty) page for more details about our active programs. 32 | 33 | ## Preferred Languages 34 | 35 | We prefer all communications to be in English. 36 | 37 | ## Policy 38 | 39 | Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/opensource/security/cvd). 40 | 41 | 42 | -------------------------------------------------------------------------------- /SUPPORT.md: -------------------------------------------------------------------------------- 1 | # TODO: The maintainer of this repo has not yet edited this file 2 | 3 | **REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project? 4 | 5 | - **No CSS support:** Fill out this template with information about how to file issues and get help. 6 | - **Yes CSS support:** Fill out an intake form at [aka.ms/onboardsupport](https://aka.ms/onboardsupport). CSS will work with/help you to determine next steps. 7 | - **Not sure?** Fill out an intake as though the answer were "Yes". CSS will help you decide. 8 | 9 | *Then remove this first heading from this SUPPORT.MD file before publishing your repo.* 10 | 11 | # Support 12 | 13 | ## How to file issues and get help 14 | 15 | This project uses GitHub Issues to track bugs and feature requests. Please search the existing 16 | issues before filing new issues to avoid duplicates. For new issues, file your bug or 17 | feature request as a new Issue. 18 | 19 | For help and questions about using this project, please **REPO MAINTAINER: INSERT INSTRUCTIONS HERE 20 | FOR HOW TO ENGAGE REPO OWNERS OR COMMUNITY FOR HELP. COULD BE A STACK OVERFLOW TAG OR OTHER 21 | CHANNEL. WHERE WILL YOU HELP PEOPLE?**. 22 | 23 | ## Microsoft Support Policy 24 | 25 | Support for this **PROJECT or PRODUCT** is limited to the resources listed above. 26 | -------------------------------------------------------------------------------- /imgs/acc.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Azure/ai-solution-accelerators-list/31f971fd1fc937af5430bbd137af88240b4a7475/imgs/acc.jpg -------------------------------------------------------------------------------- /imgs/ai.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Azure/ai-solution-accelerators-list/31f971fd1fc937af5430bbd137af88240b4a7475/imgs/ai.png --------------------------------------------------------------------------------