Made-With-ML
Learn how to develop, deploy and iterate on production-grade ML applications.
Goku Mohandas' course on taking ML from a notebook to a reliable production system: testing, CI/CD, monitoring, and the engineering most courses ignore.
From the README
git clone https://github.com/GokuMohandas/Made-With-ML.git .
- 💡 First principles: before we jump straight into the code, we develop a first principles understanding for every machine learning concept.
- 💻 Best practices: implement software engineering best practices as we develop and deploy our machine learning models.
- 📈 Scale: easily scale ML workloads (data, train, tune, serve) in Python without having to learn completely new languages.
- ⚙️ MLOps: connect MLOps components (tracking, testing, serving, orchestration, etc.) as we build an end-to-end machine learning system.
- 🚀 Dev to Prod: learn how to quickly and reliably go from development to production without any changes to our code or infra management.
- 🐙 CI/CD: learn how to create mature CI/CD workflows to continuously train and deploy better models in a modular way that integrates with any stack.
Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.
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Made-With-ML has 48.8k stars on GitHub. It has been forked 7.7k times. Made-With-ML is written mainly in Jupyter Notebook. It has been in active development since 2018. Made-With-ML is available under the MIT license. Its main topics are data-engineering, data-quality, data-science, deep-learning.
Read the full guideWhat is Made-With-ML?
Learn how to develop, deploy and iterate on production-grade ML applications.
Is Made-With-ML open source?
Made-With-ML is an open-source project. It is released under the MIT license.
Is Made-With-ML free?
Yes. Made-With-ML is free and open source — you can use, modify and self-host it.
What license does Made-With-ML use?
Made-With-ML is available under the MIT license.
What language is Made-With-ML written in?
Made-With-ML is written mainly in Jupyter Notebook.
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