Breaking: Open-Source MLOps Pipeline Debuts with Full Lifecycle Automation and Monitoring Stack

Pipeline Covers Model Training to Observability

A complete, production-ready MLOps pipeline has been released to the open-source community, integrating data versioning, experiment tracking, hyperparameter tuning, model serving, containerization, CI/CD, Kubernetes deployment, drift monitoring, and cost tracking. The project, authored by avinashmnth2507-dev, is available on GitHub and aims to streamline the entire machine learning lifecycle from development to operations.

Breaking: Open-Source MLOps Pipeline Debuts with Full Lifecycle Automation and Monitoring Stack — Source: dev.to

The pipeline uses DVC for data versioning, MLflow for experiment tracking, Optuna for hyperparameter tuning, FastAPI for model serving, Docker for containerization, GitHub Actions for CI/CD (pushing to GHCR), Kubernetes with Minikube for orchestration, PSI for drift monitoring, and Prometheus plus Grafana for infrastructure monitoring. A FinOps cost tracking feature is also included.

Key Features

Data versioning with DVC
Experiment tracking with MLflow
Hyperparameter tuning with Optuna
Model serving via FastAPI
Containerization with Docker
CI/CD via GitHub Actions → GHCR
Kubernetes deployment (Minikube)
Drift monitoring using PSI
Monitoring stack: Prometheus + Grafana
FinOps cost tracking

Known Issue: Grafana Dashboard Shows No Data

The developer reports that the Grafana dashboard currently displays "No data" due to a Prometheus scrape issue in the local Minikube cluster. "I'm actively debugging – suggestions welcome," avinashmnth2507-dev said.

"The Grafana dashboard is currently showing 'No data' due to a Prometheus scrape issue in my local Minikube cluster. I'm actively debugging – suggestions welcome." — avinashmnth2507-dev, project creator

Background

MLOps – the combination of machine learning, DevOps, and data engineering – has become critical for organizations wanting to operationalize ML models reliably. Despite numerous tools available, stitching them into a cohesive pipeline remains challenging, especially for small teams or individual practitioners.

This project addresses that gap by providing a unified, hands-on reference implementation. It demonstrates how to integrate leading open-source tools into a single workflow, from data versioning to model monitoring.

What This Means

For ML engineers and data scientists, this pipeline lowers the barrier to building observability and automation into their projects. By offering a ready-to-use template with full code on GitHub, the project enables rapid prototyping and adaptation.

The inclusion of proactive drift monitoring (PSI) and observability (Prometheus/Grafana) helps teams detect model degradation early. The FinOps component also addresses a growing need for cost awareness in cloud-native ML deployments.

Call for Community Feedback

The developer is actively seeking feedback on several components: the GitHub Actions workflow, Kubernetes manifests (deployment.yaml, service.yaml), and the drift monitoring implementation. All constructive criticism is welcome.

"I'm especially interested in feedback on the GitHub Actions workflow, Kubernetes manifests, and the drift monitoring implementation. Open to all constructive criticism," avinashmnth2507-dev stated. The repository includes detailed instructions for reproducing the pipeline locally.

Explore the full pipeline on GitHub.

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