The Best Lightwood Alternatives in 2026

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

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

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

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

FEDOT automates machine learning model development through its modeling framework.

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

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.

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

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.

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

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

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

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.

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

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

DeepSpeed optimizes distributed training and inference for deep learning models.

This Scikit-learn extension accelerates compatible applications through optimized implementations.

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

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

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