How to Create CI/CD Pipeline for GenAI Application using Jenkins

Introduction

Generative AI (GenAI) applications are becoming increasingly popular in enterprises, powering use cases like chatbots, text summarization, code generation, and more. However, developing these applications is only half the battle — ensuring smooth deployment, scalability, and continuous improvement is where CI/CD (Continuous Integration/Continuous Deployment) pipelines play a critical role.

In this blog, we’ll walk you through how to set up a Jenkins-based CI/CD pipeline for a GenAI application, covering code integration, testing, containerization, and automated deployment.

Why CI/CD for GenAI Applications?

GenAI apps often involve:

• Frequent model updates (fine-tuning or prompt engineering).
• Integration with APIs like OpenAI, Anthropic, or HuggingFace.
• Deployment across multiple environments (dev, staging, prod).

CI/CD helps by:

• Automating builds, testing, and deployments.
• Reducing human error.
• Enabling faster iterations.
• Architecture of the CI/CD Pipeline

Our pipeline will look like this:

• Source Code: Code hosted on GitHub/GitLab.
• Build Stage: Python/Node environment setup, dependency installation.
• Test Stage: Unit tests for API endpoints, model response checks.
• Containerization: Dockerize the GenAI application.
• Deployment: Push to AWS/GCP/Azure or Kubernetes.

Step 1: Prerequisites

Before starting, ensure you have:

• Jenkins installed (on VM, Docker, or Kubernetes).
• GitHub repository with GenAI application code.
• Docker installed on Jenkins server.
• Cloud environment or server to deploy the app.

Step 2: Example GenAI Application (FastAPI + OpenAI)

Here’s a minimal FastAPI app using OpenAI API:

# app/main.py
from fastapi import FastAPI
import openai
import os

app = FastAPI()

openai.api_key = os.getenv("OPENAI_API_KEY")

@app.get("/generate")
def generate(prompt: str):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=100
    )
    return {"output": response["choices"][0]["text"]}

Step 3: Dockerfile:

We’ll containerise the application so Jenkins can deploy it anywhere.

# Dockerfile
FROM python:3.10-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

 

Step 4: Jenkins Pipeline Script (Jenkinsfile)

This Jenkinsfile automates build → test → docker build → deploy.

pipeline {
    agent any

    environment {
        DOCKER_IMAGE = "genai-app:latest"
        REGISTRY = "your-dockerhub-username/genai-app"
    }

    stages {
        stage('Checkout') {
            steps {
                git branch: 'main', url: 'https://github.com/your-repo/genai-app.git'
            }
        }

        stage('Build') {
            steps {
                sh 'pip install -r requirements.txt'
            }
        }

        stage('Test') {
            steps {
                sh 'pytest tests/' // Ensure you have test cases for API
            }
        }

        stage('Docker Build') {
            steps {
                sh 'docker build -t $DOCKER_IMAGE .'
            }
        }

        stage('Push to Registry') {
            steps {
                withCredentials([usernamePassword(credentialsId: 'dockerhub-cred', usernameVariable: 'USER', passwordVariable: 'PASS')]) {
                    sh 'echo $PASS | docker login -u $USER --password-stdin'
                    sh 'docker tag $DOCKER_IMAGE $REGISTRY:latest'
                    sh 'docker push $REGISTRY:latest'
                }
            }
        }

        stage('Deploy') {
            steps {
                sshagent(['server-ssh-cred']) {
                    sh 'ssh user@your-server "docker pull $REGISTRY:latest && docker run -d -p 8000:8000 $REGISTRY:latest"'
                }
            }
        }
    }
}

Step 5: Testing Your Pipeline

• Commit and push code to GitHub.
• Jenkins auto-triggers pipeline.
• Watch stages (checkout → test → dockerize → deploy).
• Access your app at http://your-server-ip:8000/generate?prompt=Hello

Best Practices for GenAI CI/CD

• Use secrets management (Jenkins credentials store, Vault, AWS Secrets Manager) for API keys.
• Run integration tests on AI responses (e.g., non-empty, within latency).
• Implement rolling updates or blue-green deployments for production.
• Add monitoring & logging (Prometheus, ELK, OpenTelemetry) for tracking model performance.

Conclusion

Building a GenAI application is exciting, but without proper automation, deployments can become error-prone and time-consuming. Using Jenkins CI/CD, you can streamline your workflow — from code changes to containerized deployments — ensuring your GenAI app is always production-ready.

With the setup we discussed, you now have a fully automated pipeline that can:

Pull the latest code.
Run tests.
Build Docker images.
Deploy to servers/cloud.
This approach not only saves time but also ensures faster iterations, higher reliability, and smoother scaling for your Generative AI applications.