How do you create, Dockerize, and deploy a complete Machine Learning application using Kubernetes, MLflow, and Kubeflow?

In this video, we build a complete end-to-end local MLOps workflow using a Python Iris classification application.

You’ll see how an ML application moves from model training and experiment tracking to containerization, Kubernetes, and Kubeflow Pipelines — all running locally.

The complete workflow includes:

Python ML Application
→ ML Model Training
→ MLflow Experiment Tracking
→ Docker Container
→ Kubernetes
→ Kubeflow Pipelines
→ MLflow Model & Experiment Tracking

Everything in this demo runs locally using Docker Desktop, Kubernetes/Kind, Kubeflow, and MLflow.

This video is designed as a practical MLOps tutorial for anyone who wants to understand how machine learning applications can be containerized, orchestrated, deployed, and tracked using modern MLOps tools.

Tools used:
• Python
• Scikit-learn
• MLflow
• Docker
• Docker Desktop
• Kubernetes
• Kind
• Kubeflow
• Kubeflow Pipelines

If you’re learning MLOps, Machine Learning deployment, Kubernetes, MLflow, or Kubeflow, this video gives you a complete practical workflow to follow.

If you want to learn this entire MLflow/Kubeflow deployment end to end, I am creating a series starting October 2nd, 2026. You should check the link of the series in the description of the video.

Github Repo:
https://github.com/ranashivam/build-complete-local-MLOps-platform/tree/main/Day21

Build complete local MLOPS Platform Series:
https://www.youtube.com/playlist?list=PLT4gxH7QZwYg

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