GokuMohandas/Made-With-ML

Learn how to develop, deploy and iterate on production-grade ML applications.

data-engineeringdata-qualitydata-sciencedeep-learningdistributed-mldistributed-trainingllmopsllmsmachine-learningmlops

License & maintenance

MIT is a permissive license: free to use commercially with attribution.

Last code push 7 months ago.

	Repository	Stars	Language	License	Activity
1	eugeneyan/applied-ml📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.	★ 30.6k	—	MIT	Stale
2	aws/amazon-sagemaker-examplesExample 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.	★ 11k	Jupyter Notebook	Apache-2.0	Active
3	keras-team/kerasDeep Learning for humans	★ 64.4k	Python	Apache-2.0	Active
4	streamlit/streamlitStreamlit — A faster way to build and share data apps.	★ 46k	Python	Apache-2.0	Active
5	gradio-app/gradioBuild and share delightful machine learning apps, all in Python. 🌟 Star to support our work!	★ 43.8k	Python	Apache-2.0	Active
6	explosion/spaCy💫 Industrial-strength Natural Language Processing (NLP) in Python	★ 34k	Python	MIT	Active
7	eriklindernoren/ML-From-ScratchMachine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aim	★ 33k	Python	MIT	Stale
8	Lightning-AI/pytorch-lightningPretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.	★ 31.4k	Python	Apache-2.0	Active