The Best EvalML Alternatives in 2026

AutoGluon provides tools for automated machine learning and model development.

doddle-model is an in-memory machine learning library for Scala, built on Breeze with a scikit-learn-like API using typeclasses.

OpenMetricLearning provides metric learning pipelines, models, and a model zoo.

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

PyTorch Lightning helps pretrain and fine-tune AI models across one or many GPUs with minimal code changes.

Prodmodel supports building, testing, deploying, and iterating on data science pipelines.

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

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.

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.

DeepSpeed optimizes distributed training and inference for deep learning models.

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

Knet provides a deep learning framework developed at Koç University for building models.

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.

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.

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