Learn how to combine machine learning with software engineering to develop, deploy and maintain production ML applications.
MLOps concepts are interweaved and cannot be run in isolation, so be sure to complement the code in this repository with the detailed MLOps lessons.
- Lessons: https://madewithml.com/#mlops
- Code: GokuMohandas/mlops-course
| 🎨 Design | 💻 Developing | ♻️ Reproducibility |
| Product | Packaging | Git |
| Engineering | Organization | Pre-commit |
| Project | Logging | Versioning |
| 🔢 Data | Documentation | Docker |
| Exploration | Styling | 🚀 Production |
| Labeling | Makefile | Dashboard |
| Preprocessing | 📦 Serving | CI/CD |
| Splitting | Command-line | Monitoring |
| Augmentation | RESTful API | Systems design |
| 📈 Modeling | ✅ Testing | ⎈ Data engineering |
| Baselines | Code | Data stack |
| Evaluation | Data | Orchestration |
| Experiment tracking | Models | Feature store |
| Optimization |
python3 -m venv venv
source venv/bin/activate
python3 -m pip install --upgrade pip setuptools wheel
python3 -m pip install -e ".[dev]"
pre-commit install
pre-commit autoupdateIf the commands above do not work, please refer to the packaging lesson. We highly recommend using Python version
3.9.1.
tagifai/
├── data.py - data processing components
├── evaluate.py - evaluation components
├── main.py - training/optimization operations
├── predict.py - inference components
├── train.py - training components
└── utils.py - supplementary utilitiespython tagifai/main.py elt-data
python tagifai/main.py optimize --args-fp="config/args.json" --study-name="optimization" --num-trials=10
python tagifai/main.py train-model --args-fp="config/args.json" --experiment-name="baselines" --run-name="sgd"
python tagifai/main.py predict-tag --text="Transfer learning with transformers for text classification."uvicorn app.api:app --host 0.0.0.0 --port 8000 --reload --reload-dir tagifai --reload-dir app # dev
gunicorn -c app/gunicorn.py -k uvicorn.workers.UvicornWorker app.api:app # prodTo cite this content, please use:
@misc{madewithml,
author = {Goku Mohandas},
title = {MLOps Course - Made With ML},
howpublished = {\url{https://madewithml.com/}},
year = {2022}
}
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