The Best AutoGL Alternatives in 2026

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

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

Archai supports neural architecture search with fast, reproducible, and modular research tools.

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

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

FEDOT automates machine learning model development through its modeling framework.

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

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

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

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.

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.

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

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

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.