Looking for something like Geni? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 23 are open source.
The Geni project provides a Clojure dataframe library that runs on Apache Spark.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | Koalas | — | Yes | — |
| 2 | Whylogs | — | Yes | — |
| 3 | jSciPy | — | Yes | Android |
| 4 | Weights & Biases | — | Yes | — |
| 5 | imbalanced-ensemble | — | Yes | — |
| 6 | Microsoft ML for Apache Spark | — | Yes | — |
| 7 | mlxtend | — | Not stated | — |
| 8 | gensim | — | Yes | — |
| 9 | H2O Sparkling Water | — | Yes | — |
| 10 | DataComPy | — | Yes | — |
Koalas implements the pandas DataFrame API on Apache Spark and is deprecated; for Apache Spark 3.2 and above, use PySpark directly.
Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.
jSciPy provides Java scientific computing tools for signal processing, FFT, filtering, power spectral density, and DCT.
Weights & Biases supports model training, fine tuning, and management from experimentation through production.
Class-imbalanced machine learning is supported by imbalanced-ensemble, a toolbox for ensemble methods and datasets.
SynapseML provides distributed machine learning algorithms for Spark through its Python library.
mlxtend adds helper modules for Python data analysis and machine learning libraries.
Python developers can use gensim for topic modeling and related natural language processing tasks.
Sparkling Water brings H2O machine learning functionality into an Apache Spark cluster.
DataComPy compares Pandas, Polars, Spark, and Snowpark dataframes, with output that includes comparison statistics.
For Python data preparation, DataPrep offers an open-source low-code library to collect, clean, and visualize data.
Python developers can use Dash to build data apps and dashboards without requiring JavaScript.
AWS Data Wrangler connects Pandas with AWS data services and databases, including Athena, Glue, Redshift, and S3.
statsmodels provides statistical modeling and econometrics tools in Python.
Apache Hamilton helps teams build modular, testable dataflows with lineage and metadata.
Python developers can use Zipline’s event-driven library to backtest algorithmic trading strategies.
Segment Chinese text in PHP applications with jieba-php, a library for Chinese word tokenization.
This Node.js library implements Latent Dirichlet Allocation topic modeling in JavaScript.
The fastai deep learning library includes documentation and setup guidance, including instructions for use with Google Colab.
Prediction with gradient boosting regression tree models is implemented in leaves, a Go software library.
Automatic machine learning, hyperparameter tuning, and pipeline abstractions are provided by the Neuraxle software library.
crystal-fann provides Crystal language bindings for the FANN artificial neural network library.
The Pattern Recognition Toolbox provides MATLAB tools for pattern recognition and machine learning.
Netron displays neural network, deep learning, and machine learning models across formats such as ONNX, TensorFlow, and PyTorch.
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
