Project: MLOps Zoomcamp - Canadian Forest Fire Prediction
Project Overview: The task is to predict the risk of forest fires in various provinces of Canada based on environmental and geographical factors. The problem is a multi-class classification problem where the target variable has four categories: No Fire, Low Risk, Medium Risk, and High Risk. The goal is to develop a machine learning model that can accurately classify the fire risk level given the feature inputs.
Project Use Case:
- Resource allocation for firefighting efforts.
- Implementing precautionary measures during high-risk periods.
This project focuses less on experimentation and more on illustrating various tools and practices in MLOps.
Tools and Technology:
- Cloud: Azure
- Experiment Tracking & Model Registry: MLflow, Azure Blob Container
- Workflow Orchestration: Prefect, Azure Blob Container
- Model Deployment: Azure Container Registry, FastAPI, HTML, CSS, Azure Web App, Streamlit
- Monitoring: Evidently, Grafana, PostgreSQL
- Best Engineering Practices: CI/CD Pipeline (GitHub Actions), Version Control (Git), Unit Tests, Integration Tests, Linting, Code Formatting, Pre-commit Hooks
- Containerization: Docker, Docker Compose
Guide to the project:
- Programming language: Python version 3.10.14
- Operating System: Linux - Ubuntu 20.04
- Clone the project: git clone https://github.com/AbdallaAbker/MLOps_Canadian_Forest_Fire_Prediction.git
- Navigate into the project's main directory: cd MLOps_Canadian_Forest_Fire_Prediction
- Create a virtual environment and activate it: python3 -m venv .venv source .venv/bin/activate
- Install the requirements: pip install requirements.txt (This will install all the necessary dependencies for the project)
Main Directories:
- notebook
- experiment_tracking_model_registry
- workflow_orchestration
- model_deployment
- monitoring
