scikit-learn vs Open Data Hub | What are the differences? | StackShare

Open Data Hub vs scikit-learn

Overview

scikit-learn

Stacks1.3K

Followers1.1K

Votes45

GitHub Stars63.9K

Forks26.4K

Open Data Hub

Stacks6

Followers22

Votes0

scikit-learn vs Open Data Hub: What are the differences?

What is scikit-learn? Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

What is Open Data Hub? An open source project that provides open source AI tools for running large and distributed AI workloads on OpenShift Container Platform. It is an open source project that provides open source AI tools for running large and distributed AI workloads on OpenShift Container Platform. Currently, It provides open source tools for data storage, distributed AI and Machine Learning (ML) workflows and a Notebook development environment.

scikit-learn and Open Data Hub can be primarily classified as "Machine Learning" tools.

scikit-learn is an open source tool with 39.1K GitHub stars and 19.2K GitHub forks. Here's a link to scikit-learn's open source repository on GitHub.

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Detailed Comparison

scikit-learn	Open Data Hub
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.	It is an open source project that provides open source AI tools for running large and distributed AI workloads on OpenShift Container Platform. Currently, It provides open source tools for data storage, distributed AI and Machine Learning (ML) workflows and a Notebook development environment.
-	Open source project; AI tools for running large and distributed AI workloads on OpenShift Container Platform; Tools for data storage, distributed AI and Machine Learning
Statistics
GitHub Stars 63.9K	GitHub Stars -
GitHub Forks 26.4K	GitHub Forks -
Stacks 1.3K	Stacks 6
Followers 1.1K	Followers 22
Votes 45	Votes 0
Pros & Cons
Pros 26 Scientific computing 19 Easy Cons 2 Limited	No community feedback yet

What are some alternatives to scikit-learn, Open Data Hub?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.