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+# OpenHack Knowledge Mining Overview
+
+## Overview
+**This OpenHack enables participants to** add intelligent search capabilities to their applications and services, leveraging artificial intelligence to extract meaningful results from data. 
+
+**This OpenHack simulates a real-world scenario** where a travel company needs to uncover data locked up in documents and withdraw insights from that data to make key business decisions. 
+**During the “hacking”, participants will focus on:**  
+1. Exploring ways in which Azure Search can be used as the core of a search solution.
+2. Enriching the search solution through integration with Cognitive Services, Azure Machine Learning, and custom code. 
+
+**By the end of the OpenHack, participants will have built out a technical solution** that is a complete Azure machine learning-based intelligent search infrastructure that can interpret vast quantities of diverse data (i.e. documents, scanned images, and other digital artifacts).
+
+
+## Technologies
+Azure Cognitive Services, Azure Functions, Question Answering, Language Understanding Service, Form Recognizer, Azure Machine Learning
+## Prerequisites
+**Knowledge Prerequisites** 
+To be successful and get the most out of this OpenHack, it is highly recommended that participants have previous experience with either C#, JavaScript, or Python coding language and are familiar with the technologies listed above. Participants should have a basic understanding of how different services interact through APIs (Application Programing Interfaces), including REST/JSON interfaces.  
+Required knowledge of [Azure fundamentals](https://docs.microsoft.com/en-us/learn/paths/azure-fundamentals/). 
+
+**Language-Specific Prerequisites**  
+- Hands-on coding is required in at least one of the following programing languages: C#, JavaScript/Node.js, or Python.
+
+**Tooling Prerequisites**  
+To avoid any delays with downloading or installing tooling, have the following ready to go ahead of the OpenHack: 
+  - A modern laptop running Windows 10 (1703 or higher), Mac OSX (10.12 or higher), or one of [these Ubuntu versions](https://github.com/Azure/azure-functions-core-tools#linux) 
+  - Install your choice of Integrated Development Environment (IDE) software, such as Visual Studio, [Visual Studio Code](https://code.visualstudio.com/download), [Eclipse](https://www.eclipse.org/), or [IntelliJ](https://www.jetbrains.com/idea/). 
+- After installing Visual Studio Code, install the following language-specific extensions for the language you plan to use:
+- C#
+    - [.NET Core SDK](https://dotnet.microsoft.com/download)
+    - [C# Extension for Visual Studio Code ](https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csharp)
+- JavaScript
+    - [Node.js](https://nodejs.org/en/)
+- Python
+    - [Python Extension for Visual Studio Code](https://marketplace.visualstudio.com/items?itemName=ms-python.python) 
+- After installing the language-specific extensions, install the [Azure Functions](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-azurefunctions) extension. You will use this in some of the later challenges.
+- Most of the operations to create and modify Azure Search entities involve submitting JSON requests to a REST interface. You can write code in your preferred language to do this, or you can use [Postman](https://www.postman.com/downloads/) to create and submit collections of REST requests.
+
+**Development Environment Configuration**
+- None.
+
+**Links & Resources**  
+- [Azure Cognitive Search documentation](https://docs.microsoft.com/azure/search/)
+
+**Post Learning Recommendations**  
+- For a more directed learning experience, complete the [Implement knowledge mining with Azure Cognitive Searc](https://docs.microsoft.com/learn/paths/implement-knowledge-mining-azure-cognitive-search/)h learning path on Microsoft Learn.
+- Consider completing the [Microsoft Certified AI Engineer Associate certification](https://docs.microsoft.com/learn/certifications/azure-ai-engineer/).
+
+## Challenge Overview
+**Challenge 1: A Question of Knowledge**  
+In this challenge, you will use Microsoft Question Answering cognitive service to build and publish a knowledge base. 
+- Learning objectives:
+    - Create a question answering knowledge base that includes suitable question and answer pairs
+    - Write code that uses the REST API to query the knowledge base
+    
+**Challenge 2: The Search Begins**  
+In this challenge, you will create a datasource, index, and indexer and demonstrate code that successfully retrieves the information.
+- Learning objectives:
+    - Create an Azure Search Index
+    - Use the SDK or REST API to submit a range of search queries using both simple and full syntax
+    
+**Challenge 3: Expanding the Search**  
+In this challenge, you will update the index and demonstrate code that successfully retrieves information. 
+- Learning objectives:
+    - Add built-in cognitive skills to an Azure Search index to return:
+        - Key Phrases  
+        - Entities (including links)  
+        - Sentiment (especially reviews)  
+        
+**Challenge 4: Getting the Full Picture**  
+In this challenge, you will use the OCR skill to expand the index to extract AI-generated descriptions of images embedded in documents. 
+- Learning objectives:
+    - Add built-in cognitive skills to an Azure Search index to return:
+        - Image Descriptions and Tags
+        - OCR extracted Text
+        
+**Challenge 5: What is the Frequency?**  
+In this challenge, you will create a web API custom skill for your Azure Search index.
+- Learning objectives:
+    - Create a custom skill to find the top ten most frequent words
+    - Incorporate your custom skill into your web content index
+    
+**Challenge 6: The Search for Relevance**  
+In this challenge, you will create return search results based on synonyms and display suggestions and autocomplete options. 
+- Learning objectives:
+    - Add Synonyms to Azure Search index to ensure relevant results
+    - Implement query suggestions / autocomplete
+    - Add scoring profiles that boost documents in results
+
+**Challenge 7: Knowledge preservation**  
+In this challenge, you will modify the index skillset to generate knowledge store assets. Browse the blobs and tables in the knowledge store using the Storage Explorer interface. 
+- Learning objectives:
+    - Implement knowledge stores.
+    
+**Challenge 8: Finding Your Form**
+In this challenge, you will train a Form Recognizer model and integrate into a new Azure Search index. 
+- Learning objectives:
+    - Create a custom skill that calls the Forms Understanding Service
+    
+**Challenge 9: Use Your Intelligence**
+In this challenge, you will publish a machine learning model as a web service to predict claim probability. 
+- Learning objectives:
+    - Build a custom skill that consumes a custom machine learning model
+
+## Value Proposition
+- This OpenHack provides a real-world context of Azure services for knowledge mining with Azure Search.
+- Learn common knowledge mining and intelligent search scenarios.
+- AI-Powered Knowledge Mining helps businesses make better decisions through more robust data extraction and analysis.
+- Improved search functionality to decrease time-to-find relevant data.
+
+
+## Technical Scenarios
+- Search – Improving the ability to ingest data from various sources, intelligently sift through that data using core Azure AI services like Azure Search and Cog Services, and then analyze that data
+- Data modeling – Create custom ML models that learn from data and continuously improve search