H2O vs Deepkit | What are the differences? | StackShare

Deepkit vs H2O

Overview

H2O

Stacks122

Followers211

Votes8

GitHub Stars7.3K

Forks2.0K

Deepkit

Stacks2

Followers8

Votes0

H2O vs Deepkit: What are the differences?

Developers describe H2O as "H2O.ai AI for Business Transformation". 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. On the other hand, Deepkit is detailed as "The collaborative and analytical AI training suite". It is the collaborative and analytical training suite for insightful, fast, and reproducible modern machine learning. All in one cross-platform desktop app for you alone, corporate or open-source teams.

H2O and Deepkit can be categorized as "Machine Learning" tools.

H2O is an open source tool with 4.68K GitHub stars and 1.7K GitHub forks. Here's a link to H2O's open source repository on GitHub.

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Detailed Comparison

H2O	Deepkit
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.	It is the collaborative and analytical training suite for insightful, fast, and reproducible modern machine learning. All in one cross-platform desktop app for you alone, corporate or open-source teams.
-	Real-time UI and collaboration; Unified experiments; Model debugger; Any framework, all languages; Job scheduling; Pipeling; Docker and GPU support; Docker and GPU support; Offline first; Git integration / CI
Statistics
GitHub Stars 7.3K	GitHub Stars -
GitHub Forks 2.0K	GitHub Forks -
Stacks 122	Stacks 2
Followers 211	Followers 8
Votes 8	Votes 0
Pros & Cons
Pros 2 Very fast and powerful 2 Auto ML is amazing 2 Highly customizable 2 Super easy to use Cons 1 Not very popular	No community feedback yet
Integrations
No integrations available	Docker Python TensorFlow Git Keras PyTorch

What are some alternatives to H2O, Deepkit?

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.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

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.

PredictionIO

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