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How to Build CI/CD Pipeline: GitHub to AWS

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 Creating a CI/CD pipeline to deploy a project from GitHub to AWS can be done using various AWS services like AWS CodePipeline, AWS CodeBuild, and optionally AWS CodeDeploy or Amazon ECS for application deployment. Below is a high-level guide on how to set up a basic GitHub to AWS pipeline: Prerequisites AWS Account : Ensure access to the AWS account with the necessary permissions. GitHub Repository : Have your application code hosted on GitHub. IAM Roles : Create necessary IAM roles with permissions to interact with AWS services (e.g., CodePipeline, CodeBuild, S3, ECS, etc.). AWS CLI : Install and configure the AWS CLI for easier management of services. Step 1: Create an S3 Bucket for Artifacts AWS CodePipeline requires an S3 bucket to store artifacts (builds, deployments, etc.). Go to the S3 service in the AWS Management Console. Create a new bucket, ensuring it has a unique name. Note the bucket name for later use. Step 2: Set Up AWS CodeBuild CodeBuild will handle the build proces
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Python Abstract Classes to Learn Quickly

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Here's the best example of how to understand the abstract classes of python quickly. An abstract class means it is a skeleton. You cannot instantiate abstract classes. For Example: The abstract class name is 'dog' o = dog() It gives an error. Since the ' dog' is an abstract class. I am sharing with you a clear understanding of abstract class. So that you can tell confidently about these classes. The syntax for Abstract Class from abc , import ABC , abstractmethod  class <class name>(ABC): @abstractmethod  def <method name>():  #abstract class definition Python Abstract Class Explanation from abc import ABC, abstractmethod class AbsBaseClass(ABC): def __init__(self): print("Abstract class") @abstractmethod def abMeth(self): pass def conMeth(self): print("I'm a concrete method") class Derived(AbsBaseClass): def abMeth(self): print("I'm redefined") o=Derived() o.abMeth() o.conMeth() References Star Python Certification
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