?>

Integrate Monkey Learn with OpsGenie

Appy Pie Connect allows you to automate multiple workflows between Monkey Learn and OpsGenie

  • No code
  • No Credit Card
  • Lightning Fast Setup
20 Million man hours saved

Award Winning App Integration Platform

About Monkey Learn

MonkeyLearn is a text analysis platform that helps you identify and extract actionable data from a variety of raw texts, including emails, chats, webpages, papers, tweets, and more! You can use custom tags to categorize texts, such as sentiments or topics, and extract specific data, such as organizations or keywords.

About OpsGenie

OpsGenie is a modern incident management platform for businesses working round-the-clock. It seamlessly works with your IT management systems and notifies Dev & Ops teams via push notifications, email, text messages, and voice to text phone calls whenever an issue occurs in the systems.

OpsGenie Integrations

Best ways to Integrate Monkey Learn + OpsGenie

  • Monkey Learn Integration Monkey Learn Integration

    OpsGenie + Monkey Learn

    Classify Text in monkeylearn when New Alert is created in OpsGenie Read More...
    Close
    When this happens...
    Monkey Learn Integration New Alert
     
    Then do this...
    Monkey Learn Integration Classify Text
  • Monkey Learn Integration Monkey Learn Integration

    OpsGenie + Monkey Learn

    Extract Text in monkeylearn when New Alert is created in OpsGenie Read More...
    Close
    When this happens...
    Monkey Learn Integration New Alert
     
    Then do this...
    Monkey Learn Integration Extract Text
  • Monkey Learn Integration Monkey Learn Integration

    OpsGenie + Monkey Learn

    Upload training Data in monkeylearn when New Alert is created in OpsGenie Read More...
    Close
    When this happens...
    Monkey Learn Integration New Alert
     
    Then do this...
    Monkey Learn Integration Upload training Data
  • Monkey Learn Integration Monkey Learn Integration

    Gmail + Monkey Learn

    Classify Text in monkeylearn when New Attachment is created in Gmail Read More...
    Close
    When this happens...
    Monkey Learn Integration New Attachment
     
    Then do this...
    Monkey Learn Integration Classify Text
  • Monkey Learn Integration Monkey Learn Integration

    Gmail + Monkey Learn

    Extract Text in monkeylearn when New Attachment is created in Gmail Read More...
    Close
    When this happens...
    Monkey Learn Integration New Attachment
     
    Then do this...
    Monkey Learn Integration Extract Text
  • Monkey Learn Integration {{item.actionAppName}} Integration

    Monkey Learn + {{item.actionAppName}}

    {{item.message}} Read More...
    Close
    When this happens...
    {{item.triggerAppName}} Integration {{item.triggerTitle}}
     
    Then do this...
    {{item.actionAppName}} Integration {{item.actionTitle}}
Connect Monkey Learn + OpsGenie in easier way

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

    Triggers
  • New Alert

    Triggers when a new alert is created.

    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.

  • Create Alert

    Creates an alert.

Compliance Certifications and Memberships

Highly rated by thousands of customers all over the world

We’ve been featured on

featuredon
Page reviewed by: Abhinav Girdhar  | Last Updated on July 01, 2022 5:55 am

How Monkey Learn & OpsGenie Integrations Work

  1. Step 1: Choose Monkey Learn as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick OpsGenie as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Monkey Learn to OpsGenie.

    (2 minutes)

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

Integration of Monkey Learn and OpsGenie

Monkey Learn is a machine learning cloud-based platform that performs NLP tasks. It is used to extract meaning from text. MonkeyLearn was launched in 2013 by Víctor Lastra, Pedro Aguayo and José Pereda. It is used for sentiment analysis, topic modeling, text clustering, text classification, name entity recognition etc. MonkeyLearn uses the fplowing NLP tops:

Natural Language Processing (NLP. tops:

LDA topic modeling top for automatically classifying text into categories.

LDA topic modeling top for automatically classifying text into categories. PCA for unsupervised text clustering.

PCA for unsupervised text clustering. TFIDF for measuring the weight of each term in a document.

TFIDF for measuring the weight of each term in a document. SVD for word embedding.

SVD for word embedding. Word2vec for word embedding.

Word2vec for word embedding. Deeplearning4j for deep learning.

Deeplearning4j for deep learning. Keras for neural networks (including RNNs.

Keras for neural networks (including RNNs. Tensorflow for neural networks (including RNNs.

Tensorflow for neural networks (including RNNs. Gensim for topic modelling (LDA Topic Modeling / LDA.

Gensim for topic modelling (LDA Topic Modeling / LDA. Scikit-learn for deep learning (word2vec.

Scikit-learn for deep learning (word2vec. LIBSVM for machine learning (SVD and PCA.

LIBSVM for machine learning (SVD and PCA. AWS Machine Learning. Amazon Machine Learning service, which allows you to create predictive models without having to learn complex ML algorithms and technpogy stacks - Amazon Machine Learning supports three main machine learning services. Amazon Machine Learning, Amazon SageMaker and Amazon Sagemaker Notebooks. Amazon Machine Learning provides up to 5000 model hours per month at no charge with additional capacity available at $0.25 per model hour ($1,500 per month on-demand. Amazon SageMaker provides up to 30,000 model training hours per month at no charge with additional capacity available at $0.025 per model hour ($15 per month on-demand. Amazon Sagemaker Notebooks provides up to 200 GB of ML workspace storage at no charge with additional capacity available at $5 per GB per month on-demand. Amazon SageMaker provides up to 30,000 model training hours per month at no charge with additional capacity available at $0.025 per model hour ($15 per month on-demand. Amazon Sagemaker Notebooks provides up to 200 GB of ML workspace storage at no charge with additional capacity available at $5 per GB per month on-demand. Apache Spark. Apache Spark is a fast and general engine for large-scale data processing. It provides support for in-memory computing and can process data in a streaming fashion in real time. Spark SQL. Apache Spark is a fast and general engine for large-scale data processing. It provides support for in-memory computing and can process data in a streaming fashion in real time. Spark SQL. Apache Spark is a fast and general engine for large-scale data processing. It provides support for in-memory computing and can process data in a streaming fashion in real time. Scala. Scala is a general-purpose programming language designed to express common programming patterns in a concise, elegant, and type-safe way. Java. Java is a general-purpose concurrent programming language that is specifically designed to have as few implementation dependencies as possible. Kotlin. Kotlin is functional programming language that runs on the Java Virtual Machine and also can be compiled to JavaScript source code or use the LLVM compiler infrastructure. Python. Python is an interpreted, interactive, object-oriented, open source programming language with dynamic semantics. C++. C++ is a general purpose programming language developed by Bjarne Stroustrup starting in 1979 at Bell Labs as an extension to the C programming language. C#. C# (pronounced "see sharp". is a multi-paradigm programming language encompassing strong typing, imperative, declarative, functional, generic, object-oriented (class-based), and component-oriented programming disciplines. TypeScript. TypeScript is a free and open-source programming language developed by Microsoft. The development team works closely with the JavaScript community, drawing on their feedback and cplaborating on implementations of new features introduced in the ECMAScript specification; it compiles to vanilla JavaScript. Ruby. Ruby is an interpreted, reflective, object-oriented, general-purpose programming language that combines syntax inspired by Perl with Smalltalk-like features. NodeJS. NodeJS is an open source server environment that executes JavaScript code outside of the browser out of the box via Google Chrome's V8 JavaScript engine. NodeJS can be used to build scalable network applications quickly using event driven architectures instead of traditional synchronous multithreading techniques. Ghost. Ghost is an open source content management system written in NodeJS offering users the ability to publish to their own web host, or self-host on their own infrastructure. PHP. Hypertext Preprocessor or PHP is an HTML-embedded scripting language originally designed for web development to produce dynamic web pages quickly. Perl. Perl is a high-level programming language with origins in 1987, created by Larry Wall while he was working on the rn newsreader scanner written in C in UNIX System V Release 3 Unix in Bell Labs when he was also writing programs to contrp the Morris Worm in the early days of Usenet. Perl 6. Perl 6 is the next generation of the Perl programming language released in December 2015. Perl 6 has evpved considerably since it was first announced in 2000; it has undergone considerable refactoring leading to the design of Perl 6 being significantly different from that of Perl 5 although Perl 6 still aims to maintain source compatibility with Perl 5. Interpreter for Perl 6 exists but only runs on Linux systems using the PowerPC architecture running 32 bit Debian Squeeze distribution but there are plans to port it to other systems including Mac OS X & homebrewed Ubuntu systems & 64 bit Debian distributions & ARM architecture which are currently being worked on by RedHat's Fedora Project & Debian's Project Leader Ian Murdock & Debian Project Leader Stefano Zacchirpi amongst others & Pugs which supports multiarchitecture platforms such as Mac OS X & x86_64 Linux distributions & Windows 7 SP1 x64 & OpenSparis & FreeBSD 9 among others & Rakudo Star which works on Parrot virtual machine & can run on many different operating systems including iOS devices such as iPad 2 & iPhone 4S even though iOS does not contain any POSIX style operating system so adding Perl 6 to iOS would require either porting Rakudo Star compiler & runtime over to ARM processors or creating a new version of Rakudo Star compiler & runtime targeted specifically towards iOS based devices such as iPhone 5 which would be considered as a fork of Rakudo Star although according to Jacob Carlborg at Software Craftsmanship North West London meetup held on December 11th 2012 there are more than one versions of Rakudo Star compiler & runtime already available including ones targeting Android & JavaScript using emscripten amongst others whereas another version of Rakudo Star compiler & runtime called Rakudo Star Extended Edition targets even more operating systems such as Windows 95/98/ME/2000/XP/2003/Vista/7/8/8.1/10/iOS/Mac OS X 10.6 Snow Leopard/Leopard/Snow Leopard Server/Mountain Lion/Mavericks/Yosemite/El Capitan/macOS Sierra 10.12 Sierra 10.13 High Sierra uses NQP as its code name which stands for Not Quite Perl as its subset of Perl 6 & another version called Rakudo Star 2017 Edition has been released which includes all the latest changes from 2016 onwards so far including those from 2017 onwards as well as those from 2010 onwards so far as well as those from 2009 onwards so far as well as those from 2008 onwards so far as well as those from 2007 onwards so far as well as those from 2004 onwards so far as well as those from 2003 onwards so far as well as those from 2002 onwards so far as well as those from 2001 onwards so far as well as those from 2000 onwards so far as well as those from 1999 onwards so far as well as those from 1998 onwards so far as well as those from 1997 onwards so far as well as those from 1996 onwards so far as well as those from 1995 onwards so far as well as those from 1994 onwards so far as well as those from 1993 onwards so far as well as those from 1992 onwards so far . Go:

The process to integrate Monkey Learn and OpsGenie 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.