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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Aerosolve vs Open Data Hub

Aerosolve vs Open Data Hub

OverviewComparisonAlternatives

Overview

Aerosolve
Aerosolve
Stacks27
Followers73
Votes0
GitHub Stars4.8K
Forks565
Open Data Hub
Open Data Hub
Stacks6
Followers22
Votes0

Aerosolve vs Open Data Hub: What are the differences?

Aerosolve: A machine learning package built for humans (created by Airbnb). This library is meant to be used with sparse, interpretable features such as those that commonly occur in search (search keywords, filters) or pricing (number of rooms, location, price). It is not as interpretable with problems with very dense non-human interpretable features such as raw pixels or audio samples; 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.

Aerosolve and Open Data Hub belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Aerosolve are:

  • A thrift based feature representation that enables pairwise ranking loss and single context multiple item representation.
  • A feature transform language gives the user a lot of control over the features
  • Human friendly debuggable models

On the other hand, Open Data Hub provides the following key features:

  • 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

Aerosolve is an open source tool with 4.62K GitHub stars and 583 GitHub forks. Here's a link to Aerosolve's open source repository on GitHub.

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Detailed Comparison

Aerosolve
Aerosolve
Open Data Hub
Open Data Hub

This library is meant to be used with sparse, interpretable features such as those that commonly occur in search (search keywords, filters) or pricing (number of rooms, location, price). It is not as interpretable with problems with very dense non-human interpretable features such as raw pixels or audio samples.

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.

A thrift based feature representation that enables pairwise ranking loss and single context multiple item representation.;A feature transform language gives the user a lot of control over the features;Human friendly debuggable models;Separate lightweight Java inference code;Scala code for training;Simple image content analysis code suitable for ordering or ranking images
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
4.8K
GitHub Stars
-
GitHub Forks
565
GitHub Forks
-
Stacks
27
Stacks
6
Followers
73
Followers
22
Votes
0
Votes
0

What are some alternatives to Aerosolve, Open Data Hub?

TensorFlow

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

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

PyTorch

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

Keras

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

Kubeflow

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

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

Polyaxon

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

Streamlit

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

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

H2O

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.

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