The Best numpy-ML Alternatives in 2026

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

metric-learn provides Python implementations of metric learning algorithms.

hyperopt-sklearn uses hyperparameter optimization to select machine learning algorithms for scikit-learn.

BayesO provides a Python package for Bayesian optimization algorithms and related modeling tasks.

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

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

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

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

Determined manages distributed model training, hyperparameter tuning, experiment tracking, and resources.

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

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.

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

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

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

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.

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

EvalML builds, optimizes, and evaluates machine learning pipelines with automated data checks and objective functions.

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

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

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