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# Microsoft Fabric: Start with Real-Time Intelligence

This repo is a companion to this session at Microsoft AI Tour, a worldwide tour of events.

> Learn more about Microsoft AI Tour on the official website.

![Session cover image with a bright "AI" text in 3D over a blue and purple abstract background.](https://raw.githubusercontent.com/microsoft/aitour-get-started-with-fabric/main/lab/media/session-cover.png)

## Session Description

Analysis of real-time data streams is a critical capability for any modern data analytics solution. You can use the Real-Time Intelligence capabilities of Microsoft Fabric to ingest, query, and process streams of data.

## Learning Outcomes

The sample data you'll use in this lab is a set of bicycle data, containing information about bike ID, location, timestamp, and more. You'll learn how to set up resources, ingest data, set alerts on the data, and visualize the data to extract insights.

In this lab you will learn how to:

- Set up your environment
- Get data in the Real-Time hub
- Transform events
- Publish an event stream
- Set an alert on the event stream
- Create a KQL query
- Create a Real-Time dashboard
- Explore data visually in the Real-Time dashboard
- Create a Power BI report from a KQL query

## Technology Used

- Microsoft Fabric
- Real-Time Intelligence
- Kusto Query Language (KQL)
- Power BI

## Business Value

- Be proactive instead of reactive with your data, thanks to Real-Time Intelligence.
- Show how businesses can get insights in Real-Time from their data.

## Additional Resources and Continued Learning
You can find additional resources, including the slides of the presentation, [here](./SESSION_RESOURCES.md).

If you will present this workshop, you can find the [session delivery resources here](./session-delivery-resources/README.md).

## Responsible AI
Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at [https://aka.ms/RAI](https://aka.ms/RAI). Microsoft’s approach to responsible AI is grounded in our AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the [Azure OpenAI service Transparency note](https://learn.microsoft.com/legal/cognitive-services/openai/transparency-note?tabs=text) to be informed about risks and limitations.

The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. [Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview) provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. Within Azure AI Studio, the Content Safety service allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following [quickstart documentation](https://learn.microsoft.com/azure/ai-services/content-safety/quickstart-text?tabs=visual-studio%2Clinux&pivots=programming-language-rest) guides you through making requests to the service.

Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using [Performance and Quality and Risk and Safety evaluators](https://learn.microsoft.com/azure/ai-studio/concepts/evaluation-metrics-built-in). You also have the ability to create and evaluate with [custom evaluators](https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk#custom-evaluators).

You can evaluate your AI application in your development environment using the [Azure AI Evaluation SDK](https://microsoft.github.io/promptflow/index.html). Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the azure ai evaluation sdk to evaluate your system, you can follow the [quickstart guide](https://learn.microsoft.com/azure/ai-studio/how-to/develop/flow-evaluate-sdk). Once you execute an evaluation run, you can [visualize the results in Azure AI Studio](https://learn.microsoft.com/azure/ai-studio/how-to/evaluate-flow-results).

## Content Owners

<table>
<tr>
 <td align="center"><a href="http://learnanalytics.microsoft.com">
 <img src="https://github.com/dikodev.png" width="100px;" alt="Chris Testa-O'Neill
"/> 
 Someleze Diko
</a> 
 <a href="https://github.com/dikodev" title="talk">📢</a> 
 </td>
 <td align="center"><a href="http://learnanalytics.microsoft.com">
 <img src="https://github.com/jcardif.png" width="100px;" alt="Chris Testa-O'Neill
"/> 
 Josh Ndemenge
</a> 
 <a href="https://github.com/jcardif" title="talk">📢</a> 
 </td>
</tr></table>

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