Deploy a VM with IoT Edge configured with one command

Deploy a VM with IoT Edge configured with one command

The ability to quickly spin up an Azure VM with IoT Edge configured out of the box is very convenient for testing or a lab environment. For this purpose, we have an [**Azure online training**](https://onlineitguru.com/microsoft-azure-training.html...

The ability to quickly spin up an Azure VM with IoT Edge configured out of the box is very convenient for testing or a lab environment. For this purpose, we have an Azure online training Marketplace Ubuntu w/ IoT Edge offer. However, some users mentioned to me that it is a bit flaky with automation and hard to extend with other custom operations. I did a bit of digging to see if I could tweak this and shared an improvement on Github. Community members expressed interest in knowing how the improvement worked under the covers, so here are the gory (not really) details.

ARM + cloud-init = Done

Cloud-init is a widely used industry approach to customize a Linux installation after the first boot. Microsoft Azure training supports cloud-init for several popular Linux distributions. I picked Ubuntu 18.04, a Tier 1 OS for IoT Edge. Using cloud-init, I was able to easily add the Microsoft Debian repo which hosts IoT Edge and Azure Moby packages, and install them at first boot (see cloud-init.txt ). Cloud-init is also a convenient extension point to add other packages or commands you might want installed or executed on your IoT Edge VM at boot.

Most of you are likely familiar with Azure Resource Manager (ARM) it is the Azure native way to describe your cloud infrastructure, and a natural choice to define the components needed to spin up a ready-to-use Azure VM. The interesting bit for us is that ARM supports cloud-init, I just needed to put the contents of cloud-init.txt in the VM’s customdata field.

1 (step) is better than 3
The current Azure Marketplace offer requires three steps accept terms, create VM and finally, run the configuration script that sets the device connection string. I wanted to see if I could do this in a single step.
The challenge was to dynamically incorporate the user supplied connection string in the cloud-init config, which is then set as the value for custom data field of VM’s ARM template. In my web search I stumbled on a way to do this using ‘, variables(‘parameterName’), ‘ pattern in the cloud-init file. It also had a link to the genoneline.py script that helped transform the cloud-init.txt file into a single line as required by the ARM template.

Putting it together
Using this method, I was able to produce a single liner that goes from zero to a VM with IoT Edge sending data to the cloud:
If you prefer GUIs, you launch this ARM template in your Azure Portal as well: Deploy to Azure online training Hyderabad.
In the westu2 region, I was able to see data flowing to IoT Hub in under 3 minutes of executing the command (YMMV). Hope this was helpful, reply in the comments below if you have any feedback.
And finally, PRs are welcome if you have ideas for improvements!

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Microsoft Azure for Beginners | What Is Azure? | Microsoft Azure Training | Intellipaat

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Azure Cognitive Services

Azure Cognitive Services

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Azure Cognitive Services—New types added to Named Entity Recognition
The Named Entity Recognition (NER) v3 service (in preview) of Cognitive Services Text Analytics now has new entity types available, expanding the detection of general and personal information entities found in text Azure online training.

This update introduces model version 2020-02-01 and includes support for:

General types PERSONTYPE, PRODUCT, EVENT, GEOPOLITICAL ENTITY (GPE), and SKILL.
Personal information types PERSON, ORGANIZATION, AGE, DATE, EMAIL, URL, IP ADDRESS, and PHONE NUMBER (US only).
Only English (en) is supported in this update and is available in the following regions Australia East, Central Canada, Central US, East Asia, East US, East US 2, North Europe, Southeast Asia, South Central US, UK South, West Europe, and West US

How to use Named Entity Recognition in Text Analytics
The Text Analytics API lets you takes unstructured text and returns a list of disambiguated entities, with links to more information on the web. The API supports both named entity recognition (NER) and entity linking.

Entity Linking
Entity linking is the ability to identify and disambiguate the identity of an entity found in text (for example, determining whether an occurrence of the word Mars refers to the planet, or to the Roman god of war). This process requires the presence of a knowledge base in an appropriate language, to link recognized entities in text. Entity Linking uses Wikipedia as this knowledge base.
Named Entity Recognition (NER)
Named Entity Recognition (NER) is the ability to identify different entities in text and categorize them into pre-defined classes or types. For example: people, places and organizations.

Named Entity Recognition versions and features
The Text Analytics API offers two versions of Named Entity Recognition - v2 and v3. Version 3 (Public preview) provides increased detail in the entities that can be detected and categorized.
Entity types
Named Entity Recognition v3 provides expanded detection across multiple types. Currently, NER v3 can recognize the following categories of entities:

General
Personal Information
For a detailed list of supported entities and languages, see the NER v3 supported entity types article here Microsoft Azure training.

Request endpoints
Named Entity Recognition v3 uses separate endpoints for NER and entity linking requests. Use a URL format below based on your request:

Entity linking

Model versioning
Version 3 of the Text Analytics API lets you choose the model version that is most current for your data. Use the optional model-version parameter to select the version of the model that is desired for your requests. If this parameter isn't specified the API will default to latest, the latest stable version. Even though you can use the newest model-version in any request, only some features are updated in each version. The table below describes which features have been updated in each model version:

Each response from the v3 endpoints includes a model-version field specifying the model version that was used.
{
"documents": […]
"errors": []
"model-version": "2019-10-01"
}
Sending a REST API request
Preparation
You must have JSON documents in this format: ID, text, language.

Each document must be under 5,120 characters, and you can have up to 1,000 items (IDs) per collection. The collection is submitted in the body of the request.

Structure the request
Create a POST request. You can use Postman or the API testing console in the following links to quickly structure and send one.
Version 3 uses separate endpoints for NER and entity linking requests. Use a URL format below based on your request:

Example NER request
The following is an example of content you might send to the API. The request format is the same for both versions of the API.
{
"documents": [
{
"language": "en",
"id": "1",
"text": "I had a wonderful trip to Seattle last week."
}
]
}
Post the request
Analysis is performed upon receipt of the request. See the data limits section in the overview for information on the size and number of requests you can send per minute and second.

The Text Analytics API is stateless. No data is stored in your account, and results are returned immediately in the response.

View results
All POST requests return a JSON formatted response with the IDs and detected entity properties.

Output is returned immediately. You can stream the results to an application that accepts JSON or save the output to a file on the local system, and then import it into an application that allows you to sort, search, and manipulate the data.
Example v3 responses
Version 3 provides separate endpoints for NER and entity linking. The responses for both operations are below.

Example NER response
{
"documents": [{
"id": "1",
"entities": [{
"text": "Seattle",
"type": "Location",
"offset": 26,
"length": 7,
"score": 0.80624294281005859
}, {
"text": "last week",
"type": "DateTime",
"subtype": "DateRange",
"offset": 34,
"length": 9,
"score": 0.8
}]
}],
"errors": [],
"modelVersion": "2019-10-01"
}
Example entity linking response
{
"documents": [{
"id": "1",
"entities": [{
"name": "Seattle",
"matches": [{
"text": "Seattle",
"offset": 26,
"length": 7,
"score": 0.15046201222847677
}],
"language": "en",
"id": "Seattle",
"url": "https://en.wikipedia.org/wiki/Seattle",
"dataSource": "Wikipedia"
}]
}],
"errors": [],
"modelVersion": "2019-10-01"
}

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Microsoft Azure Training | Azure Training For Beginners | Azure Tutorial | Intellipaat

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Why should you opt for a Azure career?

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