https://github.com/kyryl-opens-ml/ml-in-production-practice
Practice for Machine Learning in Production course
https://github.com/kyryl-opens-ml/ml-in-production-practice
data inference-api infrastructure llm ml mlops monitoring pipelines platform
Last synced: 5 months ago
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Practice for Machine Learning in Production course
- Host: GitHub
- URL: https://github.com/kyryl-opens-ml/ml-in-production-practice
- Owner: kyryl-opens-ml
- License: apache-2.0
- Created: 2024-03-06T01:34:57.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-03T03:43:30.000Z (about 1 year ago)
- Last Synced: 2025-06-03T15:27:43.226Z (12 months ago)
- Topics: data, inference-api, infrastructure, llm, ml, mlops, monitoring, pipelines, platform
- Language: Python
- Homepage: https://edu.kyrylai.com/courses/ml-in-production
- Size: 11.3 MB
- Stars: 12
- Watchers: 0
- Forks: 5
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ML in Production Practice
This repository contains practical exercises and reference implementations for the [ML in Production](https://edu.kyrylai.com/courses/ml-in-production) course.
## Setup
1. Clone the repo and create a Python **3.10+** virtual environment.
2. Each module is a self-contained example with its own dependencies. Check the module's `README.md` or `PRACTICE.md` for installation instructions.
3. Format the code with `ruff format` and run `ruff check` to verify style.
## Project structure
```
.
├── module-1/ # containerization and infrastructure basics
├── module-2/ # data management and labeling
├── module-3/ # model training workflows
├── module-4/ # pipeline orchestration
├── module-5/ # serving with FastAPI
├── module-6/ # large model optimisation and load testing
├── module-7/ # monitoring and observability
├── module-8/ # additional production topics
└── docs/ # images used in documentation
```
Each module is self‑contained with its own `README.md`, assignments and reference code. You can dive into any module independently or work through them sequentially.
## DeepWiki Summaries
Concise overviews of each module are available on [DeepWiki](https://deepwiki.com/kyryl-opens-ml/ml-in-production-practice). This community-maintained wiki highlights key takeaways and links back to the source materials in this repository.
## Versioning
A protected `2024-version` branch preserves the 2024 and early 2025 edition of this course. The main branch contains the most up‑to‑date materials.
## Support
- [Create an issue](../../issues) if you encounter problems or have feature requests.
- Join the [course Discord](https://discord.gg/5NF2NAsGEM) to ask questions.
- Visit the [blog](https://kyrylai.com/blog/) for additional articles.
- See the [course page](https://edu.kyrylai.com/courses/ml-in-production) for curriculum details.