Google AutoML Tables vs Gradio

Overview

Google AutoML Tables

Stacks23

Followers64

Votes0

Gradio

Stacks37

Followers24

Votes0

GitHub Stars40.4K

Forks3.1K

Google AutoML Tables vs Gradio: What are the differences?

What is Google AutoML Tables? 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.

What is Gradio? *GUIs for Faster ML Prototyping and Sharing *. It allows you to quickly create customizable UI components around your TensorFlow or PyTorch models, or even arbitrary Python functions. Mix and match components to support any combination of inputs and outputs.

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

Some of the features offered by Google AutoML Tables are:

Increases model quality
Easy to build models
Easy to deploy

On the other hand, Gradio provides the following key features:

Customizable Components
Multiple Inputs and Outputs
Sharing Interfaces Publicly & Privacy

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Detailed Comparison

Google AutoML Tables	Gradio
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 allows you to quickly create customizable UI components around your TensorFlow or PyTorch models, or even arbitrary Python functions. Mix and match components to support any combination of inputs and outputs.
Increases model quality; Easy to build models; Easy to deploy; Flexible user options; Doesn’t require a large annual licensing fee	Customizable Components; Multiple Inputs and Outputs; Sharing Interfaces Publicly & Privacy
Statistics
GitHub Stars -	GitHub Stars 40.4K
GitHub Forks -	GitHub Forks 3.1K
Stacks 23	Stacks 37
Followers 64	Followers 24
Votes 0	Votes 0
Integrations
Google App Engine Google Cloud Dataflow	Jupyter TensorFlow PyTorch Matplotlib scikit-learn

What are some alternatives to Google AutoML Tables, Gradio?

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.