Amazon Elastic Compute Cloud (Amazon EC2) is a web service provides secure, reliable, scalable, and low-cost computational resources. It gives developers the tools to build virtually any web-scale application.
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.Monkey Learn Integrations
Amazon EC2 + Monkey LearnClassify Text in monkeylearn when New Scheduled Event is created in Amazon EC2 Read More...
Amazon EC2 + Monkey LearnExtract Text in monkeylearn when New Scheduled Event is created in Amazon EC2 Read More...
Amazon EC2 + Monkey LearnUpload training Data in monkeylearn when New Scheduled Event is created in Amazon EC2 Read More...
Amazon EC2 + Monkey LearnClassify Text in monkeylearn when New Instance is created in Amazon EC2 Read More...
Amazon EC2 + Monkey LearnExtract Text in monkeylearn when New Instance is created in Amazon EC2 Read More...
It's easy to connect Amazon EC2 + Monkey Learn without coding knowledge. Start creating your own business flow.
Triggers when a new instance is created.
Triggers when a new event is scheduled for one of your instances.
Start Stop or Reboot Instance
Classifies texts with a given classifier.
Extracts information from texts with a given extractor.
Uploads data to a classifier.
Amazon EC2 is a web service that provides on-demand cloud computing. It enables users to launch virtual servers on-demand and pay only for the duration they are used. Amazon EC2 makes it possible to scale the resources required to run applications and databases as needed, and automatically scale them back when they are not in use, saving users both time and money.
Monkey Learn is an online machine learning platform that can be used by developers and data scientists without any machine learning skills. Users can build machine learning models using our library of pre-trained algorithms or upload their own data, train their models and apply them to their own datasets. Monkey Learn allows users to create data products without writing code, and to make predictions using machine learning algorithms.
Amazon EC2 and Monkey Learn Integration
Amazon provides the best way to host the data science workflows. The AWS cloud computing infrastructure combined with Monkey Lears’ algorithms makes it easy to build data science applications without the need of extensive knowledge of data science.
Amazon EBS enables you to store your data in Amazon S3, making it available across all of the instances in your account. You can take advantage of the speed, durability, availability and scalability of Amazon S3, while taking advantage of the flexibility and convenience of the instance storage.
Monkey Learn requires an Amazon S3 bucket to store your output files. However, Monkey Let doesn’t have support for classic Amazon ECS AMIs. You must upload your outputs manually to the S3 bucket or use an AMI that has support for Amazon EBS disks.
In this tutorial we will create an AMI that uses an Amazon EBS disk rather than classic instance storage so you can use the same AMI for both Monkey Learn and other AWS services like Elastic Beanstalk or RDS. The fplowing steps assume that you already have set up an EC2 instance and created an IAM user with rights to use S3. If you need help with that check out this tutorial on how to create an IAM user with access to EBS vpume snapshots.
sudo apt-get update sudo apt-get install -y qemu-kvm libvirt-bin ubuntu-vm-builder virtinst sudo virt-install --name ec2-instance-1 --vcpus=1 --memory=512 --disk path=/tmp/ubuntu/image_vp_0,bus=virtio,format=qcow2 --network bridge=br0 --graphics none --cdrom /home/ec2-user/image_vp_0.iso --os-type linux --os-variant ubuntu1604 --os-release xenial --private-network bridge=br0 --private-network=10.20.0.0/24 --public-hostname ec2-instance-1 --location http://aws.amazon.com/marketplace/pp/B00ZV9RTMK?tag=awsadminblog-20 --noautoconspe 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 sudo apt - get update sudo apt - get install - y qemu - kvm libvirt - bin ubuntu - vm - builder virtinst sudo virt - install -- name ec2 - instance - 1 -- vcpus = 1 -- memory = 512 -- disk path = / tmp / ubuntu / image_vp_0 , bus = virtio , format = qcow2 -- network bridge = br0 -- graphics none -- cdrom / home / ec2 - user / image_vp_0 . iso -- os - type linux -- os - variant ubuntu1604 -- os - release xenial -- private - network bridge = br0 -- private - network = 10.20.0.0 / 24 -- public - hostname ec2 - instance - 1 -- location http . //aws.amazon.com/marketplace/pp/B00ZV9RTMK?tag=awsadminblog-20 -- noautoconspe
Once the AMI is created, it will be stored in your S3 bucket in an images directory with a name like ec2-instance-1_20170402T153348Z.manifest where “20170402T153348Z” is the timestamp when it was created (in UTC. To see which AMIs are available in your account go to the AWS Marketplace at https://aws.amazon.com/marketplace/. Search for “ubuntu 1604” and select Ubuntu Server 1604 LTS (HVM), SSD Vpume Type (sc1. from the drop down list. Make sure to select “EU (Ireland)” from the Location drop down list:
Note. When selecting the AMI make sure that it is based on HVM since Monkey Learn currently doesn’t support PVM (Paravirtualized. AMIs. Also, make sure that you selected one that supports EBS vpumes. Once you found an AMI you can click on it and then select “Use this image”:
The process to integrate Amazon EC2 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.