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

AutoGluon vs Comet.ml

Overview

Comet.ml

Stacks12

Followers50

Votes3

AutoGluon

Stacks8

Followers38

Votes0

Comet.ml vs AutoGluon: What are the differences?

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; AutoGluon: *AutoML Toolkit for Deep Learning *. It automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on image, text, and tabular data.

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

AutoGluon is an open source tool with 1.72K GitHub stars and 193 GitHub forks. Here's a link to AutoGluon's open source repository on GitHub.

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Detailed Comparison

Comet.ml	AutoGluon
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.	It automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on image, text, and tabular data.
-	Quickly prototype deep learning solutions for your data with few lines of code; Leverage automatic hyperparameter tuning, model selection / architecture search, and data processing; Automatically utilize state-of-the-art deep learning techniques without expert knowledge; Easily improve existing bespoke models and data pipelines, or customize AutoGluon for your use-case
Statistics
Stacks 12	Stacks 8
Followers 50	Followers 38
Votes 3	Votes 0
Pros & Cons
Pros 3 Best tool for comparing experiments	No community feedback yet
Integrations
TensorFlow Theano scikit-learn PyTorch Keras	Python Linux

What are some alternatives to Comet.ml, AutoGluon?

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.