https://github.com/vortico/mlops-course
Course materials for MLOps, focusing on deploying and managing ML systems in production.
https://github.com/vortico/mlops-course
flama machine-learning mlops mlops-workflow
Last synced: 4 months ago
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Course materials for MLOps, focusing on deploying and managing ML systems in production.
- Host: GitHub
- URL: https://github.com/vortico/mlops-course
- Owner: vortico
- License: mit
- Created: 2025-01-09T12:14:26.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-03-28T10:14:30.000Z (over 1 year ago)
- Last Synced: 2025-08-25T14:56:39.199Z (10 months ago)
- Topics: flama, machine-learning, mlops, mlops-workflow
- Language: Jupyter Notebook
- Homepage:
- Size: 6.13 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MLOps and ML Engineering Course
## Overview
Welcome to the MLOps and ML Engineering course! This repository contains practical materials and examples for learning modern Machine Learning Operations (MLOps) and engineering best practices. The course is designed to bridge the gap between data science and production-ready ML systems.
## 🚀 Quick Start
1. Clone this repository
2. Install dependencies:
```bash
make install-dev
```
3. Start the model server
```bash
make model-serve
```
## 📚 Repository Documentation
You can find the documentation in the [`docs/`](./docs/) directory:
- `cicd/` - CI/CD pipeline implementation and practices
- `dev-env/` - Python environment management with pyenv and poetry
- `make/` - Automation and Makefile usage guide
- `package/` - MLOps package structure and implementation
- `airflow/` - Airflow DAGs and configuration
- `mlflow/` - MLflow configuration and best practices
- `package/` - MLOps package structure and implementation
## 🛠 Development Tools
This project uses modern Python development tools and practices:
### Key Make Commands
```bash
airflow-start Start serving airflow
airflow-stop Start serving airflow
black Runs black
install-dev Installs the project (with dev dependencies)
install Installs the project (only main dependencies)
isort Runs isort
lint-fix Runs a linting pipeline with auto fixing: black, isort, ruff, and mypy
lint Runs linting tools
model-serve Serves the model in local environment
model-start Start serving the model container
model-stop Stop serving the model container
pyright Runs pyright
ruff Runs ruff
test Runs tests
```
### Development Environment
- **Python Version**: 3.13
- **Package Management**: Poetry
- **Code Quality**: black, isort, ruff, pyright
- **Testing**: pytest
- **Model Serving**: Flama
- **Model Deployment**: Docker
- **Model Registry**: mlflow
- **Orchestration**: airflow
## 🎯 Learning Objectives
This course covers:
- Setting up professional ML development environments
- Implementing CI/CD for ML projects
- Building production-ready ML pipelines
- Best practices for model deployment
- Code quality and testing in ML projects
## 📖 Documentation
Detailed documentation is available in the `docs/` directory:
- [CI/CD Implementation](docs/cicd/README.md)
- [Development Environment Setup](docs/dev-env/README.md)
- [Make and Automation](docs/make/README.md)
- [Package Structure](docs/package/README.md)
## 🤝 Contributing
1. Ensure you have all development dependencies installed
2. Make your changes
3. Run the full test suite:
```bash
make lint && make test
```
4. Submit your pull request
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.