MLOps Project
Welcome to the MLOps project documentation! This project demonstrates modern MLOps practices for the ITMO University MLOps course.
Overview
This project showcases:
- Data Version Control with DVC
- ML Pipeline Development
- CI/CD for ML Projects
- Model Versioning and Deployment
- Documentation as Code
Quick Links
Features
Data Version Control
We use DVC for managing datasets and ML models, enabling:
- Large file versioning
- Experiment tracking
- Reproducible ML pipelines
- Team collaboration
ML Pipeline
Our ML pipeline includes:
- Data Collection and Storage
- Data Processing and Feature Engineering
- Model Training and Validation
- Model Evaluation and Comparison
Development Practices
We follow industry best practices:
- Git Flow with DVC integration
- Automated testing and linting
- Continuous Integration/Deployment
- Documentation as Code