go-featureprocessing
A fast, sklearn-like feature processing library for Go that generates optimized transformers from struct tags.
What is go-featureprocessing?
go-featureprocessing is a Go library that provides fast, scikit-learn-like feature preprocessing for machine learning pipelines. It allows developers to define feature transformations (like scaling, encoding, vectorization) directly on struct fields using tags, then generates optimized, allocation-free transformation code. It solves the problem of slow feature processing in Go by avoiding reflection and cgo overhead while maintaining a simple, declarative API.
Go developers building machine learning pipelines, data processing systems, or high-performance applications that require efficient feature engineering. It's particularly useful for teams transitioning from Python's scikit-learn who need similar functionality in Go.
Developers choose go-featureprocessing for its exceptional performance (100ns per sample), ease of use through struct tags, and ability to generate production-ready transformers without manual optimization. Its unique selling point is combining sklearn-like convenience with Go-native speed through code generation.
Overview
🔥 Fast, simple sklearn-like feature processing for Go
Use Cases
Best For
- Building high-performance ML inference pipelines in Go
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
