Looking for something like Aerosolve? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 19 are open source.
Aerosolve provides a machine learning package with example models and documentation.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | Accord.MachineLearning | — | Not stated | Web |
| 2 | caret | — | Not stated | Web |
| 3 | Optuna | — | Not stated | Web |
| 4 | numpy-ML | — | Yes | — |
| 5 | bayeso | — | Yes | — |
| 6 | doddle-model | — | Yes | — |
| 7 | Learnergy | — | Yes | — |
| 8 | AutoGluon | — | Yes | — |
| 9 | FEDOT | — | Yes | — |
| 10 | Petastorm | — | Yes | — |
The Accord.MachineLearning package includes .NET algorithms for support vector machines, decision trees, and clustering.
The caret R package provides functions for classification and regression training.
Optuna automates hyperparameter optimization for machine learning models.
numpy-ml collects machine learning algorithms in NumPy, presented as an intentionally readable Python package.
BayesO provides a Python package for Bayesian optimization algorithms and related modeling tasks.
doddle-model is an in-memory machine learning library for Scala, built on Breeze with a scikit-learn-like API using typeclasses.
Use Learnergy, a Python library for training and working with energy-based machine learning models.
AutoGluon provides tools for automated machine learning and model development.
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.
KerasTuner searches hyperparameters to optimize machine learning models built with Keras.
AutoKeras automates development of deep learning models through a machine learning library.
Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.
hyperopt-sklearn uses hyperparameter optimization to select machine learning algorithms for scikit-learn.
mgl provides a machine learning library in Common Lisp, including neural networks and Gaussian processes.
emel provides functional machine learning tools for developers using the Erlang ecosystem.
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.
EvalML builds, optimizes, and evaluates machine learning pipelines with automated data checks and objective functions.
Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative
