MLOps for DevOps Engineers - A hands-on, project-based guide to Machine Learning Operations

Demo repository implementing an end-to-end MLOps workflow on Databricks, using Azure DevOps for CICD orchestration. Project derived from dbx basic python template

The Ops Compendium is a resource list for dataops, mlops, devops, etc, which I'm actively curating in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.

Sample Machine Learning App for MLOps Learning created by School of Devops

A collection of portable workflows, automation recipes and components for MLOps in a unified CK format. Note that this repository is outdated - please check the 2nd generation of the CK workflow automation meta-framework with portable MLOps and DevOps components here:

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An MLOps pipeline for unsupervised industrial defect detection using PaDiM, ONNX, FastAPI, Docker, Azure DevOps, and MLflow.

Tools for DevOps and MLOps. Materials and projects. New technologies and infrastructure review.

Sample Machine Learning App for MLOps Learning created by School of Devops

This demonstrates the core ideas of DevOps

Leveraging the powerful features of DevOps like CI/CD, automation, workflows and apply them to our data science projects & experiments with MLOps. The CML – Continuous Machine Learning is a very handy tool have for tracking the experiment results, collaborate with others, and automating the entire workflow.

This repository is dedicated to implementing MLOps (Machine Learning Operations) for image classification tasks. The project combines the principles of DevOps with Machine Learning to streamline the development, deployment, and maintenance of image classification models.

A comprehensive, open-source DevOps learning hub covering Linux, CI/CD, IaC, containers, cloud, and emerging technologies like GitOps and MLOps. Designed for beginners and professionals to learn, contribute, and stay ahead in DevOps.

This repository contains a collection of useful repositories and resources that I use daily for AI Engineering, Data Structures & Algorithms (DSA), Development, MLOps, Intelligent Systems, AI Agents, LLMs and DevOps.

An enterprise-ready AI video analysis platform demonstrating a complete AI strategy on Azure. Implements a full MLOps lifecycle (Azure DevOps, MLflow) for Computer Vision (YOLO) and Statistical Analysis. GenAI (RAG) synthesizes the final, defensible insights.

Simple MLOps templates for real time or batch scoring workflow using Azure Machine Learning and Azure DevOps

Repository for Docker Configs for DevOps/MLOps/DataOps

Docker for Development and Operation in WebDev, DevOps, MLOps

AI-DevOps is a hands-on repository for DevOps engineers learning AI from an infrastructure-first mindset. It covers LLM infra, RAG pipelines, vector databases, MLOps/AIOps basics, CI/CD for AI workloads, observability, security, and cost-aware cloud-native deployments with real labs and examples.

Production-ready GitHub Actions workflows, templates & best practices for CI/CD, DevOps, Security, MLOps, and more.

Deploy a GPT-2-based LLM API using Flask, Docker, Jenkins, and Kubernetes. Includes CI/CD pipeline, Kubernetes YAMLs, Helm chart, and full automation setup. Ideal for DevOps ML integration and MLOps beginners.

quantum-mlops-workbench brings DevOps best practices to quantum machine learning, providing a complete CI/CD pipeline for hybrid quantum-classical models. As GitHub reports a 3× increase in QML repositories since 2024, this toolkit addresses the critical need for reproducible quantum experiments and automated testing on real quantum hardware.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles. The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

All the code examples and documents related to MLOPs Course for devops engineers

Code snippets for LearnMLOps guides — practical MLOps examples for DevOps engineers

Solution for projects in Udacity course: Machine Learning Devops Engineer (MLOps)

This repository is to demonstrate mlops for deep learning using Devops Tools

Sample Machine Learning App for MLOps Learning created by School of Devops

This is an exercise to master git and github for future DevOps and MLOps implementations

Simple MLOps template for real time model deployments using Azure Machine Learning and Azure DevOps