The Best bayeso Alternatives in 2026

numpy-ml collects machine learning algorithms in NumPy, presented as an intentionally readable Python package.

pomegranate provides flexible probabilistic modeling tools and algorithms for Python users.

Karate Club offers algorithms for unsupervised learning on graph data in Python.

This metric-learn repository contains Python implementations of metric learning algorithms.

metric-learn provides Python implementations of metric learning algorithms.

TensorFlow Model Optimization provides Keras and TensorFlow tools for model quantization and pruning.

Use Learnergy, a Python library for training and working with energy-based machine learning models.

autoai automates regression and classification modeling in Python through model search and hyperparameter tuning.

ML-Ensemble provides Python tools for high-performance ensemble learning with scikit-learn.

PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.

FEDOT automates machine learning model development through its modeling framework.

Enables single machine or distributed training and evaluation of deep learning models.

Detecto supports building and training computer vision models with the PyTorch machine learning framework.

ML_Forgex trains, evaluates, and predicts with machine learning models on cleaned datasets.

PyTorch Geometric Temporal provides models and tools for spatiotemporal signal processing with neural learning.

Auto_ViML builds multiple machine learning models with a single line of code.

DeepSpeed optimizes distributed training and inference for deep learning models.

m2cgen converts machine learning models into native code for multiple programming languages without runtime dependencies.

KerasTuner searches hyperparameters to optimize machine learning models built with Keras.

AutoKeras automates development of deep learning models through a machine learning library.

Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.

AutoGL provides automated graph learning through a machine learning framework and toolkit.

Lightwood builds and customizes machine learning pipelines using a declarative JSON-AI syntax.

Guild AI automates and optimizes machine learning experiments while tracking each run.