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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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Career Opportunities to Write Algorithms

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Many participants in the Analytics seminar expressed opportunity in preparing algorithms for predictive analytics.
opportunities

You Need Algorithms Why

Using these algorithms, businesses can make better data-driven decisions by extracting actionable patterns and detailed statistics from large, often cumbersome data sets.

Many business people small to big expecting some kind of algorithms. So that they can save their precious time in predictive analytics.

As per IBM What are Good Benefits of Right Algorithm

  • Transform data into predictive insights to guide front-line decisions and interactions. 
  • Predict what customers want and will do next to increase profitability and retention. 
  • Maximize the productivity of your people, processes and assets. 
  • Detect and prevent threats and fraud before they affect your organization. 
  • Measure the social media impact of your products, services and marketing campaigns. 
  • Perform statistical analysis including regression analysis, cluster analysis and correlation analysis.

Summary

Algorithm making is a step by step process. The key advantages are useful to end users and taking less time in processing of application.

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