The Best doddle-model Alternatives in 2026

AutoGluon provides tools for automated machine learning and model development.

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

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.

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

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

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

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

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

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

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

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.

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.

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

mgl provides a machine learning library in Common Lisp, including neural networks and Gaussian processes.

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

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.

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

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