MLOps for engineers who'd rather grep logs than tune hyperparameters

What it does This repository is a structured curriculum for DevOps, platform, and SRE engineers who need to operate machine learning workflows without becoming data scientists. It walks you through building an employee attrition prediction model locally, then moves to enterprise orchestration with Airflow, Kubeflow, MLflow, and KServe running on Kubernetes. The emphasis is on automation, deployment, and troubleshooting rather than algorithmic theory.

The interesting bit Most MLOps resources treat infrastructure as an afterthought for data scientists; this one treats ML as another workload for infrastructure engineers to own. It anchors every lesson to a single, relatable problem—predicting attrition across roughly half a million employees—so the cognitive load stays in the pipelines and manifests, not the math.

Key highlights

• Phase 1 covers local development, data preparation, training with scikit-learn, and serving a live API via KServe.
• Phase 2 layers on production concerns: data versioning with DVC, pipeline orchestration with Airflow and Kubeflow, experiment tracking with MLflow, feature stores with Feast, and model monitoring with Evidently AI.
• Everything is built to run on Kubernetes and Docker; the tech stack is explicitly Helm-friendly and container-native.
• The repository holds code and manifests, but the detailed explanations live in linked external guides published on DevOpsCube.
• Code is Apache 2.0, though the written guides themselves are all rights reserved.

Caveats

• The promised capstone—a unified, end-to-end MLOps platform on Kubernetes—is marked as planned but not yet available.
• Because the narrative content lives outside the repo, you will be reading guides on a newsletter site rather than in GitHub-flavored Markdown.
• The scope is deliberately narrow: traditional ML with scikit-learn, not deep learning or LLM infrastructure.

Verdict Pick this up if you are a platform engineer suddenly asked to support model training and serving clusters but you do not know a feature store from a firewall rule. Look elsewhere if you need a drop-in automation framework or advanced data-science methodology.