Data Analytics Application

The project showcases containerization using Docker, CI/CD pipeline automation with Jenkins, and Kubernetes orchestration with Minikube. The repository also includes automated testing for application reliability.

Features

• Containerization: The application is containerized using Docker for consistent deployments.
• CI/CD Automation: Jenkins pipelines automate the build, test, and deployment processes.
• Kubernetes Deployment: Kubernetes YAML files manage the deployment and services on Minikube.
• Configuration Management: Ansible playbooks automate server setup and application deployment.
• Testing Framework: Automated testing with Pytest ensures application robustness.

Repository Structure

├── ansible # Ansible playbooks for configuration management 
│ ├── deploy.yaml # Deployment playbook 
│ └── inventory.ini # Ansible inventory file 
├── data # Data files for the AI application 
│ └── sample.csv 
├── k8s # Kubernetes YAML configurations 
│ ├── deployment.yaml # Kubernetes deployment configuration 
│ └── service.yaml # Kubernetes service configuration 
├── src # Source code of the AI application 
│ ├── app.py # Flask application 
│ ├── analysis.py # Data analysis script 
├── tests # Pytest test scripts 
│ └── test_analysis.py 
├── deployment_app.dockerfile # Dockerfile for building the application 
├── Jenkinsfile # Jenkins pipeline for CI/CD 
├── requirements.txt # Python dependencies 
├── README.md # Documentation

Getting Started

Prerequisites

• Python 3.8 or higher
• Docker
• Minikube
• Jenkins

Installation

Clone the repository:

git clone https://github.com/shubhambhavsar/Deployment_of_AI_Solutions.git

Set Up Python Environment

Create a virtual environment and activate it:

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install the necessary dependencies:

pip install -r requirements.txt

3. Run the Application Locally

Start the Flask application:

python src/app.py

Now you can access the application by visiting http://127.0.0.1:5000 in your browser.

Containerization with Docker

Build the Docker Image

To containerize the application, run the following command:

docker build -t data-analytics-app .

Run the Docker Container

Once the image is built, run the container:

docker run -p 5000:5000 data-analytics-app

CI/CD Pipeline with Jenkins

Set Up Jenkins

1. Install Jenkins on your local machine.
2. Install the required plugins, such as Docker and GitHub.

Define the Pipeline

The provided Jenkinsfile defines the build, test, and deploy stages. You can modify it based on your needs.

Run the Pipeline

In Jenkins, configure a new pipeline job pointing to this GitHub repository and trigger the pipeline to automate the process.

Kubernetes Deployment with Minikube

Start Minikube

To start the Minikube local cluster, run:

minikube start

Deploy the Application to Kubernetes

Apply the Kubernetes manifests to deploy the app:

kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml

Access the Application

Retrieve the Minikube IP to access the application:

minikube ip

Visit the provided IP in your browser.

Testing

To run the unit tests using Pytest, execute the following:

pytest tests/

Ensure all tests pass successfully.