Welcome to my MLOps repository! This repo is designed to guide you through the essentials of MLOps, from foundational concepts to hands-on practices for setting up mission-critical pipelines.
MLOps bridges the gap between machine learning (ML) and operational best practices, enabling scalable, reproducible, and reliable ML workflows. If you’re a DevOps engineer or SRE, you're already halfway there, as DevOps principles translate well to the MLOps workflow culture.
Hands-on experience with Git, GitHub, containerization, orchestration, cloud services, monitoring tools, data tools, Python, and relevant frameworks will be key in your MLOps journey.
The goal of this repository is to help people understand MLOps and its significance in the world of ML and software engineering.
- College students
- IT professionals with up to 5 years of experience
- Continuous learners interested in DevOps and MLOps
I'll be updating this README as we progress, adding details on the tools and technologies used in each section and providing a sequential path through the material. Below are the key sections you'll find in this repository:
An overview of MLOps, why it’s needed, and the tools and technologies we'll cover throughout this journey.
Recommendations for the best code editors for MLOps projects, including setup tips to help you hit the ground running.
This section will cover Python fundamentals relevant to MLOps, including setup and practical examples.
I welcome contributions to this project! If you’d like to collaborate, feel free to reach out. If you're interested in contributing directly, please fork this repository and submit a pull request with your contributions.
Contact: [email protected]
Thank you for visiting my repository, and happy learning!