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Build MLOps Pipelines Using Jenkins, Docker & K8s
MLOps pipelines are the backbone of modern machine learning operations, ensuring models are reliably built, tested, deployed, and maintained at scale. Combining tools like Jenkins, Docker, and Kubernetes (K8s) offers a powerful way to automate the entire ML lifecycle—from code integration to containerization and production deployment.
This article guides you through the process of building a scalable MLOps pipeline using these three core technologies, helping you streamline your ML workflows in both development and production environments.
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Why Jenkins, Docker, and Kubernetes?
Each tool in this stack plays a critical role in enabling automation, repeatability, and scalability:
• Jenkins: A popular open-source Continuous Integration/Continuous Delivery (CI/CD) automation server. It builds and tests code automatically
• Docker: A containerization platform that packages code and dependencies into portable containers, ensuring consistency across environments.
• Kubernetes (K8s): An ...
... orchestration tool that manages containerized applications, automatically handling scaling, deployment, and monitoring.
Together, these tools form a robust infrastructure for modern MLOps pipelines.
If you're aiming to master such integrations professionally, enrolling in an MLOps Training program can offer hands-on exposure to these tools in real-world scenarios.
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Step-by-Step: Build the Pipeline
1. Version Control with Git
All source code, model training scripts, and configurations should be stored in a Git repository. This allows Jenkins to trigger builds based on code changes automatically.
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2. Automated CI with Jenkins
Set up Jenkins to monitor the Git repository for updates. When new commits are pushed:
• Jenkins pulls the code.
• It installs dependencies and runs unit tests.
• If tests pass, Jenkins proceeds to build a Docker image of the ML application.
Use Jenkinsfiles to define your CI/CD pipeline stages in code, making the process reproducible and easy to update.
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3. Containerization with Docker
Once Jenkins builds the Docker image:
• All dependencies, model artifacts, and code are bundled together.
• The image is pushed to a container registry like Docker Hub or Amazon ECR.
This ensures that the environment is consistent regardless of where the image is run—dev, test, or production.
Learning this workflow can be simplified through a structured MLOps Online Course, which typically covers containerization and orchestration in depth.
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4. Model Deployment with Kubernetes
Kubernetes comes into play once the Docker image is ready. Jenkins starts a Helm chart or deployment script that tells Kubernetes to:
• Pull the latest Docker image.
• Launch the containerized model service as a pod.
• Manage scaling, health checks, and rollbacks automatically.
You can also use Kubernetes ConfigMaps and Secrets to manage environment-specific variables and credentials securely.
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5. Monitoring and Maintenance
Tools like Prometheus and Grafana can be integrated with Kubernetes to monitor your ML models in production—tracking latency, prediction errors, and system health.
Retraining can also be automated as part of the Jenkins pipeline, ensuring your models remain accurate over time.
Professionals looking to integrate these practices into production-grade systems often benefit from structured MLOps Online Training programs that focus on automation, deployment, and real-world pipeline building.
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Conclusion
Building an MLOps pipeline using Jenkins, Docker, and Kubernetes empowers teams to automate and streamline the ML lifecycle efficiently. From code to deployment, each step becomes repeatable, scalable, and maintainable. By leveraging these powerful tools, organizations can accelerate ML adoption and improve the reliability of model deployments.

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