AI / DevOps / Databases

Implementation of an MLOps Pipeline from ETL to Deployment

We present a production-grade MLOps pipeline that automates the entire machine learning lifecycle, from data ingestion to deployment in a Kubernetes environment.

By Tanish Pathania, Siddharth Vikram & Dr B Thangaraju

Updated Jul 16, 2026 5:27 PM / Published Jul 7, 2026 3:00 AM 17 min read

Frog in pipe — Lead Image © jame505th, 123RF.com

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Tanish Pathania

Tanish Pathania focuses on scalable system design and community-driven development. He has contributed to building CI/CD-integrated MLOps pipelines with cloud-native tools.
Siddharth Vikram

Siddharth Vikram specializes in distributed systems and open source toolchains. He streamlines ML lifecycle automation and production-ready deployment in MLOps projects.
Dr B Thangaraju

Dr. B. Thangaraju is a Professor at IIIT-Bangalore, where he leads the Open Source Technology Lab. With a PhD in Physics, he brings more than two decades of experience in Linux kernel programming, DevOps, and cloud-native technologies.