Machine Learning · collection
The ML Stack That Actually Ships
The frameworks, serving engines, and libraries that real machine learning work gets built on — from training the model to putting it in front of users.
SCreated by Sanjeet Pal Singh
updated 4 hours ago
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Projects in this collection
Ranked by the curator, with their reason for including each one.
1
pytorchPython
★ New102.8K GitHub starsOther
Why Sanjeet Pal Singh included it: “The default research framework for modern deep learning, where its eager, Pythonic autograd made experimentation feel like ordinary programming and most new model code lands here first.”
2
transformersPython
★ New164.9K GitHub starsApache-2.0
Why Sanjeet Pal Singh included it: “The common vocabulary for state-of-the-art models across text, vision, and audio — pretrained architectures you can load and fine-tune without reimplementing a paper.”
3
scikit-learnPython
★ New67.2K GitHub starsBSD-3-Clause
Why Sanjeet Pal Singh included it: “The workhorse for everything that isn't a neural network, with a consistent fit/predict API and sane defaults that still solve a huge share of real-world prediction problems.”
4
vllmPython
★ New91.1K GitHub starsApache-2.0
Why Sanjeet Pal Singh included it: “Closes the loop from trained model to production by making large language model inference fast and memory-efficient enough to serve at scale.”
5
tensorflowC++
★ New198.9K GitHub starsApache-2.0
Why Sanjeet Pal Singh included it: “The end-to-end platform that carried ML into production first, with a deployment ecosystem spanning servers, mobile, and the browser that still anchors a great deal of shipped ML.”