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

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

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

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

Wind data sources can be accessed from Python with windML, a framework built on NumPy, SciPy, scikit-learn, and Matplotlib.

Browse large JSONL, Parquet, and CSV datasets in VS Code with the Bread Dataset Viewer extension.

spaCy provides Python natural language processing tools for entity recognition, tagging, parsing, and word vectors.

MITIE provides a library and tools for extracting information from text.

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.

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.

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

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

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

Sparkling Water brings H2O machine learning functionality into an Apache Spark cluster.

For Go developers, GoLearn provides a collection of tools for machine learning.

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

AWS Data Wrangler connects Pandas with AWS data services and databases, including Athena, Glue, Redshift, and S3.

DataComPy compares Pandas, Polars, Spark, and Snowpark dataframes, with output that includes comparison statistics.

Koalas implements the pandas DataFrame API on Apache Spark and is deprecated; for Apache Spark 3.2 and above, use PySpark directly.

Jupyter Notebook combines live code, equations, explanatory text, visualizations, and interactive computing in a web platform.

Kinho’s Neural repository provides an API for deep learning and feedforward neural network models.

Reinforcement learning algorithms can be evaluated with Gym4ReaL, a Gymnasium-based suite of realistic environments.