omega-ml alternatives and similar packages

Based on the "Machine Learning" category.
Alternatively, view omegaml alternatives based on common mentions on social networks and blogs.

• scikit-learn

  10.0 9.9 L3 omega-ml VS scikit-learn

  scikit-learn: machine learning in Python
  

• tensorflow

  10.0 10.0 L1 omega-ml VS tensorflow

  An Open Source Machine Learning Framework for Everyone
  

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• Keras

  9.9 9.9 L2 omega-ml VS Keras

  Deep Learning for humans
  

• gym

  9.8 0.0 omega-ml VS gym

  A toolkit for developing and comparing reinforcement learning algorithms.
  

• xgboost

  9.7 9.7 L1 omega-ml VS xgboost

  Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
  

• MindsDB

  9.7 9.9 omega-ml VS MindsDB

  Platform for building AI that can learn and answer questions over federated data.
  

• PaddlePaddle

  9.6 10.0 L1 omega-ml VS PaddlePaddle

  PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
  

• gensim

  9.5 6.9 L3 omega-ml VS gensim

  Topic Modelling for Humans
  

• CNTK

  9.5 0.0 L1 omega-ml VS CNTK

  Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
  

• Prophet

  9.5 7.4 omega-ml VS Prophet

  Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
  

• MLflow

  9.5 9.9 omega-ml VS MLflow

  Open source platform for the machine learning lifecycle
  

• dspy

  9.4 9.9 omega-ml VS dspy

  DSPy: The framework for programming—not prompting—language models
  

• TFLearn

  9.1 0.0 L3 omega-ml VS TFLearn

  Deep learning library featuring a higher-level API for TensorFlow.
  

• NuPIC

  8.8 0.0 L3 omega-ml VS NuPIC

  DISCONTINUED. Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
  

• H2O

  8.7 9.2 omega-ml VS H2O

  H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
  

• Pyro.ai

  8.6 8.0 omega-ml VS Pyro.ai

  Deep universal probabilistic programming with Python and PyTorch
  

• Surprise

  8.4 5.1 L4 omega-ml VS Surprise

  A Python scikit for building and analyzing recommender systems

• srez

  8.3 0.0 L5 omega-ml VS srez

  DISCONTINUED. Image super-resolution through deep learning

• LightFM

  7.9 0.0 L4 omega-ml VS LightFM

  A Python implementation of LightFM, a hybrid recommendation algorithm.
  

• Pylearn2

  7.7 0.0 L2 omega-ml VS Pylearn2

  Warning: This project does not have any current developer. See bellow.
  

• skflow

  7.6 1.3 L4 omega-ml VS skflow

  DISCONTINUED. Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
  

• PyBrain

  7.5 0.0 L4 omega-ml VS PyBrain

  Another Python Machine Learning Library.

• Sacred

  7.5 5.3 omega-ml VS Sacred

  Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
  

• Clairvoyant

  7.1 0.0 L3 omega-ml VS Clairvoyant

  Software designed to identify and monitor social/historical cues for short term stock movement

• garak, LLM vulnerability scanner

  6.3 9.8 omega-ml VS garak, LLM vulnerability scanner

  DISCONTINUED. the LLM vulnerability scanner [Moved to: https://github.com/NVIDIA/garak]
  

• python-recsys

  6.2 0.0 L4 omega-ml VS python-recsys

  A python library for implementing a recommender system

• karateclub

  6.2 6.7 omega-ml VS karateclub

  Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

• Metrics

  6.2 0.0 omega-ml VS Metrics

  Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

• awesome-embedding-models

  6.0 0.0 omega-ml VS awesome-embedding-models

  A curated list of awesome embedding models tutorials, projects and communities.

• pydeep

  5.8 0.0 L3 omega-ml VS pydeep

  Deep learning in Python

• Crab

  5.6 0.0 L2 omega-ml VS Crab

  Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).
  

• hebel

  5.0 0.0 L2 omega-ml VS hebel

  GPU-Accelerated Deep Learning Library in Python

• seqeval

  4.7 2.6 omega-ml VS seqeval

  A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
  

• adaptive

  4.6 5.1 omega-ml VS adaptive

  :chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions
  

• Xorbits

  4.6 8.0 omega-ml VS Xorbits

  Scalable Python DS & ML, in an API compatible & lightning fast way.
  

• TrueSkill, the video game rating system

  4.2 1.4 omega-ml VS TrueSkill, the video game rating system

  An implementation of the TrueSkill rating system for Python

• pdpipe

  3.9 0.0 omega-ml VS pdpipe

  Easy pipelines for pandas DataFrames.
  

• SciKit-Learn Laboratory

  3.8 7.5 omega-ml VS SciKit-Learn Laboratory

  SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
  

• rwa

  3.8 0.0 L5 omega-ml VS rwa

  Machine Learning on Sequential Data Using a Recurrent Weighted Average

• Feature Forge

  3.5 0.0 L4 omega-ml VS Feature Forge

  A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
  

• nptyping

  3.5 0.0 omega-ml VS nptyping

  💡 Type hints for Numpy and Pandas

• Robocorp Action Server

  3.5 9.4 omega-ml VS Robocorp Action Server

  Create 🐍 Python AI Actions and 🤖 Automations, and deploy & operate them anywhere
  

• Data Flow Facilitator for Machine Learning (dffml)

  3.3 9.3 omega-ml VS Data Flow Facilitator for Machine Learning (dffml)

  DISCONTINUED. The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.
  

• brew

  3.2 0.0 L4 omega-ml VS brew

  DISCONTINUED. Multiple Classifier Systems and Ensemble Learning Library in Python.

• bodywork

  3.1 0.0 omega-ml VS bodywork

  DISCONTINUED. ML pipeline orchestration and model deployments on Kubernetes.
  

• MLP Classifier

  2.8 0.0 L4 omega-ml VS MLP Classifier

  A handwritten multilayer perceptron classifer using numpy.

• redframes

  2.7 1.4 omega-ml VS redframes

  General Purpose Data Manipulation Library

• openskill.py

  2.7 6.8 omega-ml VS openskill.py

  Multiplayer Rating System. No Friction.
  

• OptaPy

  2.7 5.5 omega-ml VS OptaPy

  OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.
  

• vowpal_porpoise

  2.5 0.0 L3 omega-ml VS vowpal_porpoise

  lightweight python wrapper for vowpal wabbit

* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.

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Popular Comparisons

• omega-ml
  vs
  MLflow
• omega-ml
  vs
  NuPIC
• omega-ml
  vs
  Prophet
• scikit-learn
  vs
  Keras
• LightFM
  vs
  Surprise