GoDS (Go Data Structures) - Sets, Lists, Stacks, Maps, Trees, Queues, and much more
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GoDS (Go Data Structures) - Sets, Lists, Stacks, Maps, Trees, Queues, and much more
An orchestration platform for the development, production, and observation of data assets.
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
Read and write Neuroglancer datasets programmatically.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
hypermatch is a high-performance ⚡ Go library designed for rapid matching of a large number of rules to events. It processes thousands of events per second 🚀 against extensive rule sets in-memory with minimal latency ⏱️.
Blazingly fast data comparison tool for Python, powered by Rust. Compare massive CSV/Parquet datasets instantly.
GTFS Data Pipeline for TfNSW Bus Datasets