Google AutoML Tables vs Gym

Overview

Google AutoML Tables

Stacks23

Followers64

Votes0

Gym

Stacks54

Followers59

Votes0

GitHub Stars36.7K

Forks8.7K

Google AutoML Tables vs Gym: What are the differences?

Developers describe Google AutoML Tables as "Automatically build and deploy machine learning models on structured data". Enables your entire team of data scientists, analysts, and developers to automatically build and deploy machine learning models on structured data at massively increased speed and scale. On the other hand, Gym is detailed as "Open source interface to reinforcement learning tasks". It is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.

Google AutoML Tables and Gym can be categorized as "Machine Learning" tools.

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Detailed Comparison

Google AutoML Tables	Gym
Enables your entire team of data scientists, analysts, and developers to automatically build and deploy machine learning models on structured data at massively increased speed and scale.	It is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.
Increases model quality; Easy to build models; Easy to deploy; Flexible user options; Doesn’t require a large annual licensing fee	Reinforcement learning; Compatible with any numerical computation library
Statistics
GitHub Stars -	GitHub Stars 36.7K
GitHub Forks -	GitHub Forks 8.7K
Stacks 23	Stacks 54
Followers 64	Followers 59
Votes 0	Votes 0
Integrations
Google App Engine Google Cloud Dataflow	Theano TensorFlow

What are some alternatives to Google AutoML Tables, Gym?

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.