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  1. Stackups
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  5. Google AutoML Tables vs NSFWJS

Google AutoML Tables vs NSFWJS

OverviewComparisonAlternatives

Overview

NSFWJS
NSFWJS
Stacks3
Followers10
Votes1
GitHub Stars8.7K
Forks578
Google AutoML Tables
Google AutoML Tables
Stacks23
Followers64
Votes0

Google AutoML Tables vs NSFWJS: What are the differences?

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; NSFWJS: NSFW detection on the client-side via Tensorflow.js. A simple JavaScript library to help you quickly identify unseemly images; all in the client's browser. Currently, it has ~90% accuracy from a test set of 15,000 test images.

Google AutoML Tables and NSFWJS can be primarily classified as "Machine Learning" tools.

NSFWJS is an open source tool with 3.18K GitHub stars and 172 GitHub forks. Here's a link to NSFWJS's open source repository on GitHub.

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Detailed Comparison

NSFWJS
NSFWJS
Google AutoML Tables
Google AutoML Tables

A simple JavaScript library to help you quickly identify unseemly images; all in the client's browser. Currently, it has ~90% accuracy from a test set of 15,000 test images.

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.

Open source
Increases model quality; Easy to build models; Easy to deploy; Flexible user options; Doesn’t require a large annual licensing fee
Statistics
GitHub Stars
8.7K
GitHub Stars
-
GitHub Forks
578
GitHub Forks
-
Stacks
3
Stacks
23
Followers
10
Followers
64
Votes
1
Votes
0
Pros & Cons
Pros
  • 1
    Very Accurate
No community feedback yet
Integrations
TensorFlow.js
TensorFlow.js
Google App Engine
Google App Engine
Google Cloud Dataflow
Google Cloud Dataflow

What are some alternatives to NSFWJS, Google AutoML Tables?

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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