Polars extension for general data science use cases

skchange provides sktime-compatible change detection and changepoint-based anomaly detection algorithms

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

A package for segmenting streaming time series data into homogenous segments. The segmentation is based on statistical change-point detection (aka online/sequential/iterative change-point detection).

Tensors and Dynamic neural networks in Python with strong GPU acceleration

nannyml: post-deployment data science in python

Python packaging and dependency management made easy

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Open source platform for the machine learning lifecycle

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

The interactive graphing library for Python ✨

An open-source, low-code machine learning library in Python

Uniform Manifold Approximation and Projection

A hyperparameter optimization framework

1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

Streamlit — A faster way to build and share data apps.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning …

ruptures: change point detection in Python

Lightning ⚡️ fast forecasting with statistical and econometric models.

🌊 Online machine learning in Python

Data, Benchmarks, and methods submitted to the M4 forecasting competition

High-Dimensional Undirected Graph Estimation

Anomaly detection related books, papers, videos, and toolboxes

An R package for kernel density estimation with parametric starts and asymmetric kernels.