Working With Azure ML workspace can be more flexible with Azure ML SDK. In this section, we are going to see how to set a workspace via AzureML SDK inside Notebook. in summary how to access the config file for the Azure ML workspace will be shown, and how to set a new workspace with [Read more about How to set Workspace inside Azure ML SDK](/content/how-to-set-workspace-inside-azure-ml-sdk/index.html)

There are always many questions about a cheat sheet to shows the existing capability for doing AI and ML in Power BI service and Desktop. In the above chart, I have summarized the capability of doing AI and ML in Power Service and Desktop for each role. I have identified three main roles in [Read more about Overview of Available AI and ML capabilities in Power BI](/content/overview-of-available-ai-and-ml-capabilities-in-power-bi/index.html)

Azure ML services has been introduced for a while, however in the last Microsoft Build, the visual interface of azure ml service has been introduced that has so much similarity to Azure ML Studio. however there are some difference between them as the created experiment in visual interface be shown in the Assets section as [Read more about New component of Azure ML Service – Visual Interface](/content/new-component-of-azure-ml-service-visual-interface/index.html)

In this series the process of how to create a simple application for facial recognition has been explained. In the last post, the process of creating Power apps for taking the photo has been explained. In this post, the process of creating face recognition function in Microsoft Flow will be explained. In the next [Read more about Create Microsoft Flow Part 2- Face Recognition by Power Apss, Microsoft Flow and Cognitive Service](/content/create-microsoft-flow-part-2-face-recognition-by-power-apss-microsoft-flow-and-cognitive-service/index.html)

Data Science Virtual Machine (DSVM) is a virtual machine on the Azure cloud that is customized for doing data science. DSVM has some pre-configured and pre-installed tools that help users to build the AI applications. DSVM will assist data science team to access a consistent setup. In this post, a brief introduction to DSVM [Read more about Data Science Virtual Machine](/content/data-science-virtual-machine/index.html)

In the last post, I have shown how to create a simple Bot. In this post, I am going to show how to create a Question and Answer Bot and embed it in a website. To start you need to create a Web App Bot in Microsoft Azure with Bot Template as Question and Answer. After creating the [Read more about Microsoft Bot -Part2: Create a Question and Answer](/content/microsoft-bot-part2-create-a-question-and-answer/index.html)

Using Microsoft Bot in applications and report make them more agile and interactive. there are many ways to use Microsoft Bot. In these posts and the next one, I will explain how to create a Microsoft Bot in Azure and then embed it in your PowerBI and messenger like Facebook messenger or Telegram. How to [Read more about Microsoft Bot -Part1: Create a sample](/content/microsoft-bot-part1-create-a-sample/index.html)

In last Post, I have explained the process of creating Event Hub, Stream Analytics, and Azure ML model for the aim of applying machine learning to streamed data. In this post, I will show how to create a function in Stream Analytics for calling the Azure ML API and finally see the result in the [Read more about Live Machine Learning in Azure Stream Analytics-part 2](/content/live-machine-learning-in-azure-stream-analytics-part-2/index.html)

In the last post, I have explained how to work with Azure Databricks. In this post, I will show: 1- Upload data in Azure data Lake Store 2- get data from Azure Data Lake Store into Azure Data Bricks 3-clean Data with Scala language 4- visualizes with R language 5- Predictive Analysis with R In [Read more about Azure data Bricks – Part2](/content/azure-data-bricks-part2/index.html)

Databricks is an analytics service based on the Apache Spark open source project. Databricks has been used for ingesting a significant amount of data. In February 2018, there is integration between Azure and Databricks. This integration provides data science and data engineer team with a fast, easy and collaborative spark-based platform in Azure [Read more about Azure data Bricks – Part1](/content/azure-data-bricks-part1/index.html)