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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

7 top initial steps you need before you start HR predictive analytics

Top criteria you need before you start analytics in the Human Resource department. I am sure you need many approvals to start analytics in HR.
hr analytics

The risks involved to start analytics in the Human Resource department

  1. You must comply with the legal requirements in which you operate as it relates to the use of people data. The reason is the analytical insights should reflect the cultural and social marks of your organization.
  2. You need to get involved all stakeholders involved and what the cost of what you're doing is relative to the benefit of doing it.
  3. Use analytics through accountable processes, one of which should be acknowledging that using predictive analytics with the workforce has the potential for negative impact, not just positive impact, Walzer said.
  4. Engage the legal department to make sure you understand any implications before you've done something, not after the fact.
  5. Assess whether the use of analytics involves sensitive areas, which it often will, Walzer said. But, she added, these are often accommodated by using reasonable safeguards.
  6. Know what data you just shouldn't collect. 
  7. One example is prescription drug use of employees. "Many employers have access to it through third-party health care providers, but the idea that you're going to bring it in poses a lot of liability to the organization

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