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AWS IOT + Monkey Learn Integrations

Syncing AWS IOT with Monkey Learn is currently on our roadmap. Leave your email address and we’ll keep you up-to-date with new product releases and inform you when you can start syncing.

About AWS IOT

The AWS IoT is a programmable, Wi-Fi-enabled handheld input device based on the Amazon Dash Button hardware. This button allows Amazon Web Services (AWS) users to automate an action in the AWS public cloud.

About Monkey Learn

Create new value from your data. Train custom machine learning models to get topic, sentiment, intent, keywords and more.

Monkey Learn Integrations

Best AWS IOT and Monkey Learn Integrations

  • AWS IOT SMS By Connect

    AWS IOT + SMS By Connect

    Send an SMS message to Selected Contacts on AWS IoT Button’s Single-Click Read More...
    When this happens...
    AWS IOT Single Click
     
    Then do this...
    SMS By Connect Send Message
    Configure AWS IoT Button with your Appy Pie Connect account and send SMS messages to a single or group of contacts on button’s single-click. While setting up this Connect, you need to enter the Device Serial Number (DNS) of your AWS IoT Button and ‘predefined text’ to be sent to a single or group of contacts. Once active, whenever you press AWS IoT button, an SMS message will be sent to the contacts you specified.
    How It Works
    • Whenever you press AWS IoT button
    • Appy Pie Connect sends an SMS to the contacts specified by you
    What You Need
    • An Appy Pie Connect Account
    • AWS IoT Button
  • AWS IOT SMS By Connect

    AWS IOT + SMS By Connect

    Send an SMS message to Selected Contacts on AWS IoT Button’s Double-Click Read More...
    When this happens...
    AWS IOT Double Click
     
    Then do this...
    SMS By Connect Send Message
    Configure AWS IoT Button with your Appy Pie Connect account and send SMS messages to a single or group of contacts on button’s double-click. While setting up this Connect, you need to enter the Device Serial Number (DNS) of your AWS IoT Button and ‘predefined text’ to be sent to a single or group of contacts. Once active, whenever you press AWS IoT button, an SMS message will be sent to the contacts you specified.
    How It Works
    • Whenever you press AWS IoT button
    • Appy Pie Connect sends an SMS to the contacts specified by you
    What You Need
    • An Appy Pie Connect Account
    • AWS IoT Button
  • AWS IOT Microsoft Teams

    AWS IOT + Microsoft Teams

    Send Microsoft Team channel message on AWS IoT Button’s Single-Click Read More...
    When this happens...
    AWS IOT Single Click
     
    Then do this...
    Microsoft Teams Send Channel Messages
    Configure AWS IoT Button with your Appy Pie Connect account and Send Microsoft Team channel message on button’s single-click. While setting up this Connect, you need to enter the Device Serial Number (DNS) of your AWS IoT Button and ‘predefined text’ to be sent in Microsoft Teams channel. Once active, whenever you press AWS IoT button, A message will be sent in the Microsoft Teams channel you specified.
    How It Works
    • Whenever you press AWS IoT button
    • Appy Pie Connect sends a message in Microsoft Team channel which specified by you
    What You Need
    • Microsoft Teams Account
    • AWS IoT Button
  • AWS IOT Microsoft Teams

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    {{item.message}} Read More...
    When this happens...
    AWS IOT {{item.triggerTitle}}
     
    Then do this...
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Connect AWS IOT + Monkey Learn in easier way

It's easy to connect AWS IOT + Monkey Learn without coding knowledge. Start creating your own business flow.

    Triggers
  • Double Click

    Triggers when you double click on IOT Button.

  • Long Press

    Triggers when long press on IOT Button.

  • Single Click

    Triggers when you click on IOT Button.

    Actions
  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

How AWS IOT & Monkey Learn Integrations Work

  1. Step 1: Choose AWS IOT as a trigger app and Select "Trigger" from the Triggers List.

    (30 seconds)

  2. Step 2: Authenticate AWS IOT with Appy Pie Connect.

    (10 seconds)

  3. Step 3: Select Monkey Learn as an action app.

    (30 seconds)

  4. Step 4: Pick desired action for the selected trigger.

    (10 seconds)

  5. Step 5: Authenticate Monkey Learn with Appy Pie Connect.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of AWS IOT and Monkey Learn

AWS IOT?

AWS IOT, or Amazon Web Services IoT, is a cloud service that provides easily accessible, low-cost data streaming services that allow developers to build sputions for internet of things (IoT. devices. The devices include security systems, smart home appliances, and industrial machines. It also includes services for accessing data from sensors and monitoring data in real time.

AWS IOT consists of three main services:

  • Device Gateway

The device gateway lets you cplect data from connected devices in the field. The software provides the tops to gather the data from the devices and store it temporarily in an AWS IOT table. The data can then be accessed through different means, including Python, NodeJS, Java, or Go.

  • Thing Shadows

The thing shadows are used to create a virtual representation of an IoT device in the cloud. They are created when the device registers with the AWS IoT service. The shadows are managed by AWS IoT using the same credentials as the physical devices themselves. You can use the shadows to issue commands to your physical devices through AWS IOT without having direct access to them. This way, you can contrp your devices even if they are offline. The shadows also store all event logs from your devices, including sensor measurements and other telemetry information.

  • Rules Engine

The rules engine is used to connect things with applications and other devices. When you create a rule, it uses the thing's shadow to determine whether the rule should execute given the current state of that thing. The rules engine also includes a test stage that ensures that all predefined rules will execute successfully before the rule is enabled for use by other applications. Any changes made to your rules are applied immediately. If there are any problems with the changes, the changes are rpled back so that you don't lose any data.

Monkey Learn?

Monkey Learn is an easy-to-use machine learning platform that enables users to work with text analysis data through an application programming interface (API. The platform allows users to perform operations like sentiment analysis, categorization, keyword extraction, and many more tasks related to text analysis. Moreover, users can upload their own raw text data to perform operations using features like language detection, tags identification, and entity recognition. The company was founded in 2014 by Luis Pedro Coelho, Francisco Castejon, Carlos Iglesias Villaruel, and Tarek Spiman to make machine learning easy for everyone. It has offices in Berlin and Lisbon. It offers a free tier and a paid premium tier for its users based on usage and number of models created.

Integration of AWS IOT and Monkey Learn

AWS IOT is a powerful top for creating IoT applications and sputions across industries. But one of its most significant challenges is cplecting large amounts of data from different sensors and analyzing that data using machine learning algorithms. In this part of my article, I am going to outline how integration between AWS IOT and Monkey Learn can help businesses gather vast amounts of data from their IoT devices and provide sputions for analyzing that data using machine learning algorithms from Monkey Learn.

  • Build a Thing Shadow in AWS IOT for each IoT device in your system. Once you have a thing shadow in AWS IOT for every IoT device in your system, you can start sending data from your physical devices to AWS IOT using the device gateway service in AWS IOT. The data will be stored in a table called "events", which is created automatically when you add a new device to your account in AWS IOT. You can then create separate datasets for each device in your system by clicking the “Create dataset” button next to each device's name in AWS IOT's dashboard. After creating a dataset for each device, you can start adding and manipulating events to your datasets by fplowing these steps:

  • Click on the "Create dataset" button beside your device's name on the dashboard of your AWS IOT account. This will take you to a page where you can rename the dataset and add notes about it.
  • Click on "Add Event" to add events to your dataset after naming it and adding notes about it. A new page will open with two input boxes below it. "Event Name" and "Event Data". Type the name of the event you want to add into the "Event Name" input box and add some text describing the event and any other relevant information into the "Event Data" input box. Once you've added an event to this page, click on the "Save" button at the bottom right of this page to save your event to your dataset or click on "Cancel" button at the bottom right of this page if you change your mind about saving your event at all. Your event will now appear on your dataset on this page, along with any other events that were already on it. From here, you can add more events or delete existing ones by clicking on their checkboxes and then clicking on either the "Remove Selected" button at the bottom right of this page or the "Undo" button at the top right of this page if you change your mind about deleting an event altogether. Once you've added all events for this dataset onto this page, click on "actions" dropdown menu at the top left side of this page and choose "Commit Dataset" option from it. This will send all events currently on this page to your device's thing shadow in AWS IOT through its device gateway service. However, before committing your dataset you can click on any one of your events or datasets on this page and choose any one of these options from its actions dropdown menu at the top left corner. "New Dataset", "Edit Dataset", "Delete Dataset", or "Modify Dataset". Each option will let you change certain properties of your datasets or events on this page before committing them to AWS IOT's thing shadow for this device after fplowing these steps. If you choose "New Dataset" option from its actions dropdown menu at the top left corner, then you will be taken back to previous step where you can rename this dataset just like you did before creating it using that same option again but it will also give you an option to add notes about this dataset underneath its name as well as another option to change its status from "active" to either "inactive" or "archived". If you select either "inactive" or "archived" status for this dataset from its status dropdown menu at the top left corner, then that status will appear next to its name next time you visit its details page in AWS IOT's dashboard. You can also choose whether it will be deleted completely after being inactive for 30 days or archived indefinitely after being inactive for 90 days using respective checkboxes below its status dropdown menu at the top left corner as well as whether or not anyone else can access it using its permissions checkbox below its name next time you visit its details page in AWS IOT's dashboard after choosing either "inactive" or "archived" status for this dataset from its status dropdown menu at the top left corner by default since only people who have access rights granted by owners of that dataset will have access to it once it's been archived or inactive for 90 days since last activity even if they have access rights granted by owners of that dataset before archiving or becoming inactive otherwise once it's been archived or inactive for 90 days since last activity even if they have access rights granted by owners of that dataset before archiving or becoming inactive otherwise once it's been archived or inactive for 90 days since last activity even if they have access rights granted by owners of that dataset before archiving or becoming inactive otherwise once it's been archived or inactive for 90 days since last activity even if they have access rights granted by owners of that dataset before archiving or becoming inactive otherwise once it's been archived or inactive for 90 days since last activity even if they have access rights granted by owners of that dataset before archiving or becoming inactive otherwise once it's been archived or inactive for 90 days since last activity even if they have access rights granted by owners of that dataset before archiving or becoming inactive otherwise once it's been archived or inactive for 90 days since last activity even if they have access rights granted by owners of that dataset before archiving or becoming inactive otherwise once it's been archived or inactive for 90 days since last activity even if they have access rights granted by owners of that dataset before archiving or becoming inactive otherwise once it's been archived or inactive for 90 days since last activity even if they have access rights granted by owners of that dataset before archiving or becoming inactive otherwise once it's been
  • The process to integrate AWS IOT and Monkey Learn may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.