Aerosolve vs Comet.ml | What are the differences? | StackShare

Aerosolve vs Comet.ml

Overview

Aerosolve

Stacks27

Followers73

Votes0

GitHub Stars4.8K

Forks565

Comet.ml

Stacks12

Followers50

Votes3

Aerosolve vs Comet.ml: 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; Comet.ml: Track, compare and collaborate on Machine Learning experiments. Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.

Aerosolve and Comet.ml can be primarily classified as "Machine Learning" tools.

Aerosolve is an open source tool with 4.58K GitHub stars and 578 GitHub forks. Here's a link to Aerosolve's open source repository on GitHub.

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Detailed Comparison

Aerosolve	Comet.ml
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.	Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.
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	-
Statistics
GitHub Stars 4.8K	GitHub Stars -
GitHub Forks 565	GitHub Forks -
Stacks 27	Stacks 12
Followers 73	Followers 50
Votes 0	Votes 3
Pros & Cons
No community feedback yet	Pros 3 Best tool for comparing experiments
Integrations
No integrations available	TensorFlow Theano scikit-learn PyTorch Keras

What are some alternatives to Aerosolve, Comet.ml?

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.

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.