MLOPs Course

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Table of Contents

• Overview
• Projects
• Datasets
• Setup & Installation
  • Clone repo
  • Create Virtual Environment
  • Install Dependencies
• Contribution
• References

Overview

This repository contains a series of hands-on projects designed to help DevOps engineers and developers understand the fundamentals of Machine Learning Operations (MLOps).

Each project focuses on a specific stage of the ML lifecycle—ranging from data preparation to model deployment—so you can build practical, job-ready skills.

Below is an overview of the upcoming projects included in this course.

Projects

mlops-course
|__ animal-classifier/
|__ ..

1. ML Basics for DevOps Engineers

Learn the essential concepts behind machine learning—algorithms, models, inferencing, and deployment at local development from a DevOps perspective.

Note: A lightweight introduction to prepare you for the hands-on projects ahead.

Project: animal-classifier/

Datasets

Most of the datasets are taken from UCI Zoo Dataset and Kaggle

Setup & Installation

Step-1: Clone the repository

git clone https://github.com/techiescamp/mlops-course.git
cd animal-classifier

Step-2: Create Virtual Environment

Windows

python -m venv venv
venv\Scripts\activate

Mac and Linux

python3 -m venv venv
source venv/bin/activate

Step-3: Install Dependencies

pip install -r requirements.txt

Step-4: Run the Model Workflow step-by-step

Execute the code according to the instructions given each of the project's readme.md file.

Contribution

Please read our Contributing Guidelines before submitting pull requests.

License

This project is under MIT Licence support.