7 GitHub Projects to Master Machine Learning

Machine learning operations, commonly referred to as MLOps, is a vast field that can sometimes feel overwhelming. However, it is the only field that is likely to thrive in a post-AI world, as we still need to deploy AI models into production.

To help you navigate your learning journey, I have ranked seven GitHub projects from beginner to expert level. These projects, created by me @kingabzpro, cover essential MLOps concepts such as deployment, automation, orchestration, and more. 

Beginner-Level Projects

1. FastAPI-for-ML

Key Skills Covered: FastAPI, Model Inference, API Development

This project is perfect for beginners who want to learn how to serve machine learning models via APIs. It walks you through building a simple FastAPI application for model inference. By the end of this project, you will understand how to expose your ML models as REST APIs, a fundamental skill for deploying ML solutions in real-world applications.

2. CICD-for-Machine-Learning

Key Skills Covered: CI/CD, GitHub Actions, Model Training & Deployment

This beginner-friendly project introduces you to continuous integration/continuous deployment (CI/CD) for machine learning. You will learn how to automate the training, evaluation, versioning, and deployment of ML models using GitHub Actions. It’s a great way to understand how automation can streamline ML workflows and reduce manual errors.

Intermediate-Level Projects

3. ML-Workflow-Orchestration-With-Prefect

Key Skills Covered: ML Pipeline, Workflow Orchestration, Notifications

This project introduces you to workflow orchestration using Prefect, a powerful tool for managing complex ML pipelines. You will learn how to streamline tasks like data ingestion, model training, and saving, while also integrating Discord notifications to monitor pipeline progress. It’s an excellent project for those looking to manage multi-step ML workflows efficiently.

4. Automating-Machine-Learning-Testing

Key Skills Covered: GitHub Actions, DeepChecks, Model Testing

Testing is a critical but often overlooked aspect of machine learning. This project teaches you how to automate ML testing using GitHub Actions and DeepChecks. You will learn how to test for issues like data integrity and model drift, ensuring your models remain reliable over time. This project is ideal for intermediate learners who want to incorporate robust testing practices into their ML workflows.

Advanced-Level Projects

5. Deploying-LLM-Applications-with-Docker

Key Skills Covered: Docker, Hugging Face, LLM, LlamaIndex, Gradio

This project focuses on deploying a document Q&A application powered by Large Language Models (LLMs) on the Hugging Face cloud using Docker. It’s a great way to learn about containerization and deploying scalable ML applications. Advanced learners will appreciate the hands-on experience with modern deployment practices for LLMs.

6. using-llama3-locally

Key Skills Covered: Ollama, LangChain, Chroma, LLMs

This project is for those who want to experiment with Llama 3 locally. You will use tools like Ollama-Python, LangChain, and Chroma to run the model and build a user interface for interaction. It’s an excellent opportunity to dive deep into the internals of LLM pipelines and understand how to work with cutting-edge models locally.

7. Deploying-Llama-3.3-70B

Key Skills Covered: vLLM, AWQ Quantization, BentoCloud, BentoML

This is the most advanced project on the list. It teaches you how to deploy Llama 3.3 70B using vLLM and AWQ quantization for optimized performance. You will also deploy the model on BentoCloud, gaining experience with large-scale model deployment in production environments. This project is ideal for experts looking to push the boundaries of LLM deployment.

Final Thoughts

 
I have worked really hard on these projects to ensure that each one teaches you something new about model serving, testing, automation, and deployments. The beginner project focuses on creating API endpoints and setting up CI/CD. The intermediate project helps you establish machine learning workflows and introduces easy ways to automate the entire machine learning lifecycle. The advanced project is all about handling large language models, building LLM applications, deploying them using Docker, and serving 70 billion parameters on the cloud.
 
 

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.