7 MLOPs Projects for Beginners

MLOps (machine learning operations) has become essential for data scientists, machine learning engineers, and software developers who want to streamline machine learning workflows and deploy models effectively. It goes beyond simply integrating tools; it involves managing systems, automating processes tailored to your budget and use case, and ensuring reliability in production. While becoming a professional MLOps engineer requires mastering many concepts, starting with small, simple, and practical projects is a great way to build foundational skills.

In this blog, we will review a beginner-friendly MLOps project that teaches you about machine learning orchestration, CI/CD using GitHub Actions, Docker, Kubernetes, Terraform, cloud services, and building an end-to-end ML pipeline.

1. Building ML Pipelines with Prefect

 
Link: Using Prefect for Machine Learning Workflows

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Prefect is a popular workflow orchestration tool that simplifies the process of building ML pipelines. In this project, you will learn how to:

• Create a machine learning workflow to automate tasks like data preprocessing, model training, and evaluation.
• Build, deploy, and execute workflows on both your local machine and the cloud using a straightforward guide.
• Monitor pipelines and handle failures efficiently, including setting up Discord alerts for pipeline errors.

This project introduces you to automated pipelines, a critical component of production-ready ML systems, and provides hands-on experience with Prefect, making it an excellent starting point for mastering workflow orchestration.

2. CI/CD for Machine Learning Projects

 
Link: A Beginner’s Guide to CI/CD for Machine Learning

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Continuous Integration and Continuous Deployment (CI/CD) is an essential MLOps practice that automates testing, validation, and deployment, enabling faster and more reliable workflows. This project will guide you through building, running, and monitoring CI/CD pipelines using GitHub Actions.

You will learn:

1. How to set up CI/CD pipelines using tools like GitHub Actions, CML, and MakeFile.
2. Key components of the workflow YAML file and how they function.
3. Automating ML workflows to test code, validate models, and deploy them to production.
4. Real-time automation, where every code change triggers retraining, validation, and redeployment of the updated ML application to the cloud.

This beginner-friendly guide focuses on hands-on implementation, making it perfect for those looking to master CI/CD and streamline their ML workflows.

3. MLOps Project with GitHub Actions

 
Link: khuyentran1401/cicd-mlops-demo

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It is a demo project for implementing CI/CD in machine learning, offering a hands-on way to explore MLOps concepts with real code. Created by Khuyen Tran, an experienced MLOps practitioner, the repository is well-documented and beginner-friendly, making it easy to follow and replicate.

You will learn:

• How to integrate GitHub Actions to automate model training and deployment.
• Version control for ML models using DVC (Data Version Control).
• Deploying a trained model to an AWS cloud platform.

This project is an excellent resource for beginners looking to understand CI/CD in machine learning and gain practical experience with MLOps workflows.

4. Deploying Large Language Models (LLMs) Using Docker

 
Link: How to Deploy LLM Applications Using Docker

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In this project, you will learn how to containerize and deploy a Large Language Model (LLM) application using Docker. It provides a hands-on approach to understanding model deployment and the use of Docker in machine learning workflows.

You will learn:

• Building a robust ML application that integrates multiple APIs for LLMs, embeddings, and data extractors.
• Creating and testing a Docker image using a Dockerfile to run the application locally.
• Deploying the LLM application to a cloud platform for production use.

This beginner-friendly project is perfect for those looking to explore model deployment while gaining practical experience with Docker and its role in machine learning applications.

5. End-to-End MLOps Project with DataTalks.Club

 
Link: DataTalksClub/mlops-zoomcamp

Image from TSFelg/mlops_zoomcamp_project

The MLOps Zoomcamp by DataTalks.Club is a free, comprehensive course that teaches you how to build end-to-end MLOps pipelines. It covers industry-standard tools, platforms, and methodologies to help you create sustainable machine learning solutions.

You will learn:

• Building ML pipelines using tools like Prefect and Airflow.
• Setting up CI/CD pipelines to automate workflows.
• Deploying models as REST APIs and monitoring them in production environments.

At the end of the course, you’ll apply your knowledge by building a complete project using the tools and platforms covered. This beginner-friendly course is one of the best starting points for mastering MLOps and transitioning from notebooks to production-ready systems.

6. MLOps Tutorial on Deploying ML Models

 
Link: Machine Learning, Pipelines, Deployment and MLOps Tutorial

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This project walks you through the essential steps for deploying machine learning models in production, providing a hands-on approach to mastering deployment workflows. It is beginner-friendly and helps build a strong foundation in MLOps.

You will learn:

• Training and developing a machine learning pipeline for deployment using a simple linear regression model.
• Building a web app with the Flask framework to generate real-time predictions using the trained ML pipeline (front-end code is not the focus).
• Creating a Docker image and container for the application.
• Publishing the container to the Azure Container Registry (ACR).
• Deploying the web app from the container onto ACR, making it publicly accessible via a web URL.

This project is perfect for beginners looking to understand the end-to-end process of deploying simple machine learning models on Azure cloud.

7. Creating Reproducible Machine Learning Projects

 
Link: prsdm/mlops-project

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This project offers a beginner-friendly introduction to MLOps with a focus on reproducibility through the Insurance Cross-Selling Prediction project. The goal is to predict which customers are most likely to purchase additional insurance products using a machine learning model.

You will learn:

• Tracking experiments and managing model versions to ensure reproducibility.
• Creating reusable pipelines for data preparation and model training.
• Using tools like MLflow to log metrics and organize artifacts effectively.
• Monitoring models in production. 

It comes with a GitHub repository that provides all the steps to reproduce the example project, and guides you through deployment and monitoring the model in production.

Final Thoughts

 
As machine learning increasingly shifts towards production, MLOps skills are becoming essential. Here are seven beginner-friendly projects that provide a hands-on approach to learning key concepts such as pipelines, CI/CD, containerization, deployment, monitoring, and reproducibility. Start with the project that interests you the most, and gradually explore the others to develop a well-rounded skill set.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.