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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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AI Project 5 things You need to be Successful

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Suppose you have got an opportunity to create a project on AI. Try implementing these five before the start. These five are Learning, Programming Language, Knowledge representation, Problem Solving, and Hardware.

Ensure These 5 Things Done, if you want to be your AI Project Successful

1. Learning Process.

What is learning? - adding knowledge to the storehouse, and improving its performance. The success of an AI program depends on two things- the extent of wisdom it has and how frequently it acquires it. Learning agents consist of four main components. They are the:
  • The Learning element - is part of the agent responsible for improving its performance. 
  • The Performance element- is the part that chooses the actions to take. 
  • Critics, that tell the learning element of how the agent is doing. 
  • The Problem generator - suggests actions that could lead to new information experiences.

2. Programming Language.

  • LISP and Prolog are the primary languages used in AI programming.
  • LISP (List Processing): LISP is an AI programming language developed by John McCarthy in 1950. LISP is a symbolic processing language that represents information in lists and manipulates lists to derive information.
  • PROLOG (Programming in Logic): Prolog, which is developed by Alain Colmeraver and P. Roussel at Marseilles University in France in the early 1970s. 
  • Prolog uses the syntax of predicate logic to perform symbolic, logical computations.

Artificial Intelligence Project Know these Five Before Start
Artificial Intelligence Project Know these Five Before Start

3. Knowledge Representation.

The quality of the result depends on how much knowledge the system acquires. You should represent the current knowledge efficiently. Hence, knowledge representation is a vital component of the system. The best-known representations schemes are:
  • Associative Networks or Semantic Networks
  • Frames
  • Conceptual Dependencies and
  • Scripts

4. Problem Solving.

The objective of this particular area of research is how to implement the procedures on AI systems to solve problems as humans do.

The inference process should also be equally fit to obtain satisfactory results. Inference-process, you can be divided into brute and heuristic search procedures.

5. Hardware.

Most of the AI programs, implemented on Von Neumann machines. However, for AI programming, dedicated workstations have emerged - classified into one of the following four categories:
  • SISD, Single Instruction Single Data Machines
  • SIMD, Single Instruction Multiple Data Machines
  • MISD, Multiple Instruction Single Data Machines
  • MIMD, Multiple Instruction Multiple Data Machines

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