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Implementation of an MLOps pipeline from ETL to deployment

In the Pipe

Article from ADMIN 90/2025

By Tanish Pathania , By Dr. B. Thangaraju , By and Siddharth Vikram

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

We deliver a production-grade machine learning operations (MLOps) [1] pipeline that accommodates the entire machine learning (ML) application lifecycle from ingestion of data to deployment in near-real time in a Kubernetes context. The pipeline has three primary phases of operation: (1) A mature extract, transform, and load (ETL) process reads in semi-structured and structured data, cleanses and transforms it, and saves the output in a scalable MongoDB instance; (2) dynamic model training reads in fresh data from MongoDB and employs reproducible, modulated scripts to achieve on-demand, decoupled computation; and (3) all parts are dockerized and shipped to Minikube hosting in a local cluster of Kubernetes.

Deployment and service configurations reveal near-real time inference by NodePort services. The system follows current DevOps techniques of modulated deployment, compositionality, and scaling and forms the basis of realizing in the near future additional extensions (e.g., continuous integration and continuous delivery (CI/CD) and observability) and deployment to the cloud, resulting in a working, cloud-agnostic method of deployment and scaling of ML systems.

ML Deployment Difficulties

Machine learning deployment in production is usually hindered by disjointed workflows, manual intervention, and a pipeline that is not automated end-to-end. Conventional ML pipelines – covering data ingestion through deployment – are normally disjointed, script-based, and not very integrated with contemporary DevOps methods. This disjointedness creates major hurdles in scaling, reproducibility, and maintainability and inhibits the operationalization of ML models in applications.

A number of systemic problems lie behind this inefficiency. Pipelines are often constructed as discrete pieces that interact with each

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