The Best OpenMetricLearning 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.

LightlyTrain supports pretraining, fine-tuning, and distillation for computer vision models.

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

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

AutoGluon provides tools for automated machine learning and model development.

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

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

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

metric-learn provides Python implementations of metric learning algorithms.

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

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.

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.

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

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

DeepSpeed optimizes distributed training and inference for deep learning models.

Deeplearning4j provides tools for training and deploying deep learning models in the JVM.

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.

PEFT provides parameter-efficient fine-tuning methods for adapting pretrained machine learning models.