The Best Bread Dataset Viewer Alternatives in 2026

Python developers can use Dash to build data apps and dashboards without requiring JavaScript.

Visualize metrics, logs, and traces from multiple sources with Grafana, an observability and data visualization platform.

Mars provides tensor-based tools for large-scale computation with NumPy, Pandas, and scikit-learn.

Class-imbalanced machine learning is supported by imbalanced-ensemble, a toolbox for ensemble methods and datasets.

Browse a gallery of interesting Jupyter notebooks on this project wiki, with examples from notebook users.

Opyrator turns machine learning code into microservices with a web API and interactive interface.

PipeRider provided code review tools for dbt data and has been superseded by Recce.

Vaex provides out-of-core DataFrames for Python to explore and visualize large tabular datasets, with support for machine learning workflows.

Python developers can use Zipline’s event-driven library to backtest algorithmic trading strategies.

Segment Chinese text in PHP applications with jieba-php, a library for Chinese word tokenization.

Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.

The Pattern Recognition Toolbox provides MATLAB tools for pattern recognition and machine learning.

This Node.js library implements Latent Dirichlet Allocation topic modeling in JavaScript.

Netron displays neural network, deep learning, and machine learning models across formats such as ONNX, TensorFlow, and PyTorch.

The fastai deep learning library includes documentation and setup guidance, including instructions for use with Google Colab.

The Geni project provides a Clojure dataframe library that runs on Apache Spark.

jSciPy provides Java scientific computing tools for signal processing, FFT, filtering, power spectral density, and DCT.

Prediction with gradient boosting regression tree models is implemented in leaves, a Go software library.

Automatic machine learning, hyperparameter tuning, and pipeline abstractions are provided by the Neuraxle software library.

crystal-fann provides Crystal language bindings for the FANN artificial neural network library.

ONNX-Scala provides an API and backend for deep and classical machine learning in Scala.

torchaudio provides data manipulation and transformation tools for audio signal processing with PyTorch.

Weights & Biases supports model training, fine tuning, and management from experimentation through production.

The anneal project is a Go machine learning compiler with graph rewriting, autodiff, and a WebGPU backend.