Explanation/Reference:
https://docs.microsoft.com/en-us/azure/mysql/overview

References:
https://docs.microsoft.com/en-us/azure/data-factory/introduction

Explanation

Step 1: Create an Azure Machine Learning Studio workspace
Step 2: Create a notebook
You can manage notebooks using the UI, the CLI, and by invoking the Workspace API.
To create a notebook
* Click the Workspace button Workspace Icon or the Home button Home Icon in the sidebar. Do one of the following:
Next to any folder, click the Menu Dropdown on the right side of the text and select Create > Notebook.
Create Notebook
In the Workspace or a user folder, click Down Caret and select Create > Notebook.
2. In the Create Notebook dialog, enter a name and select the notebook's primary language.
3. If there are running clusters, the Cluster drop-down displays. Select the cluster to attach the notebook to.
4. Click Create.
Step 3: Create a new experiment
Create a new experiment by clicking +NEW at the bottom of the Machine Learning Studio window. Select EXPERIMENT > Blank Experiment.
References:
https://docs.azuredatabricks.net/user-guide/notebooks/notebook-manage.html
https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-run-cloud-notebook

Explanation
Box 1: Azure IoT Hub
Azure IoT Hub is the cloud gateway that connects IoT devices to gather data and drive business insights and automation. In addition, IoT Hub includes features that enrich the relationship between your devices and your backend systems. Bi-directional communication capabilities mean that while you receive data from devices you can also send commands and policies back to devices.
Note on why not Azure Event Hubs: An Azure IoT Hub contains an Event Hub and hence essentially is an Event Hub plus additional features. An important additional feature is that an Event Hub can only receive messages, whereas an IoT Hub additionally can also send messages to individual devices. Further, an Event Hub has access security on hub level, whereas an IoT Hub is aware of the individual devices and can grand and revoke access on device level.
Box 2: Azure HDInsight with Apache Storm
References:
https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-compare-event-hubs
https://www.edureka.co/blog/introduction-to-real-time-analytics-with-apache-storm/

Explanation

QnA Maker: 5
If the user plans to support multiple languages, they need to have a new QnA Maker resource for each language.
LUIS: 5
If you need a multi-language LUIS client application such as a chatbot, you have a few options. If LUIS supports all the languages, you develop a LUIS app for each language. Each LUIS app has a unique app ID, and endpoint log. If you need to provide language understanding for a language LUIS does not support, you can use Microsoft Translator API to translate the utterance into a supported language, submit the utterance to the LUIS endpoint, and receive the resulting scores.
Language detection: 1
The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.
This capability is useful for content stores that collect arbitrary text, where language is unknown. You can parse the results of this analysis to determine which language is used in the input document. The response also returns a score that reflects the confidence of the model. The score value is between 0 and 1.
The Language Detection feature can detect a wide range of languages, variants, dialects, and some regional or cultural languages. The exact list of languages for this feature isn't published.
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/overview/language-support
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-language-support
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-language