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MLOps for DevOps Engineers - A hands-on, project-based guide to Machine Learning Operations
| Date | Stars |
|---|---|
| 2026-07-31 | 486 |
| 2026-08-01 | 496 |
| 2026-08-06 | 505 |
Today
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# MLOps for DevOps Engineers A hands-on, project-based guide to Machine Learning Operations built specifically for DevOps, Platform, and SRE engineers. > No ML background required. Every concept is explained through DevOps analogies you already understand. If you are completely new to MLOps, read our [DevOps to MLOps guide](https://devopscube.com/devops-to-mlops/) first. --- If you are planning to use this repo for learning MLOps, please hit the star. Thanks! --- ## Table of Contents - [Who This Is For](#who-this-is-for) - [What We Build](#what-we-build) - [Prerequisites](#prerequisites) - [Phase 1: Local Dev & Pipelines](#phase-1-local-development--data-pipelines) - [Phase 2: Enterprise Orchestration for ML](#phase-2-enterprise-orchestration-for-ml) - [Learning Path](#learning-path) - [Tech Stack](#tech-stack) - [Recommended Reading](#recommended-reading) - [License](#license) --- ## Who This Is For Most MLOps resources are written for data scientists learning infrastructure. This repo flips that. **You do not need to become a data scientist**. But just like understanding how a Java application is built makes you a better DevOps engineer, understanding how an ML model is built, trained, and served makes you effective at operating ML workloads in production. --- ## What We Build We will start with the basics of building and training a classic ML model, then work our way up to production-ready MLOps. Just like a DevOps engineer doesn't write the application but understands how it is built and deployed, an MLOps engineer doesn't need to be a data scientist. Understanding the ML workflow helps you build, automate, deploy, and troubleshoot ML systems effectively. Everything in this roadmap **runs on Kubernetes and Docker**, and tools you will use in real-world MLOps platforms. --- ## Phase 1: Local Development & Data Pipelines (Start Here) **Goal:** Build the required ML foundation by building an Employee attrition prediction model from your local systems. **Use case throughout:** Employee attrition prediction for a large organisation (~500,000 employees). One problem, end-to-end. Keeps the focus on infrastructure and operations, not data science theory. | Step | Title | Guide | |------|-------|-------| | 1 | Project Dataset Pipeline | [Read the Guide](https://newsletter.devopscube.com/p/building-a-dataset-pipeline) | | 2 | Data Preparation Stages | [Read the Guide](https://newsletter.devopscube.com/p/mlops-data-preparation) | | 3 | Training & Building the Prediction Model | [Read the Guide](https://newsletter.devopscube.com/p/mlops-training-the-model) | | 4 | From Model to Live API with KServe | [Read the Guide](https://newsletter.devopscube.com/p/deploying-model-kserve) | Code: `phase-1-local-dev/` ## Phase 2: Enterprise Orchestration for ML **Goal:** Replace local and manual ML workflows with automated, scalable, and production-ready workflows. This phase covers the following key MLOps areas: - Data Versioned workflow - Automated data pipelines - Scalable training pipelines - Experiment tracking - Scalable model deployment - Model monitoring The following guides explain each of these areas using specific open-source MLOps tools. | Step | Title | Guide | |------|-------|-------| | 1 | Data Versioning Fundamentals | [Read the Guide](https://newsletter.devopscube.com/p/mlops-data-drift-model-decay-and-dataset-versioning) | | 2 | Data Version Control (DVC) with AWS S3 | [Read the Guide](https://newsletter.devopscube.com/p/mlops-versioning-data-with-dvc)| | 3 | Data Versioning using Airflow on Kubernetes | [Read The Guide](https://newsletter.devopscube.com/p/mlops-airflow-dvc-pipeline)| | 4 | Feature Store Fundamentals Explained | [Read The Guide](https://newsletter.devopscube.com/p/mlops-feature-store) | | 5 | Hands-on Feature Store with Feast on Kubernetes | [Read The Guide](https://devopscube.com/setup-feature-store-feast-on-kubernetes/) | | 6 | Kubeflow Explained for MLOps | [Read The Guide](https://n
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matched fp:1181d8b30a44dcc0, topic:mlops, name:mlops, desc:mlops