Google AutoML Tables vs Neptune

Overview

Google AutoML Tables

Stacks23

Followers64

Votes0

Neptune

Stacks16

Followers38

Votes2

Google AutoML Tables vs Neptune: 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; Neptune: The most lightweight experiment tracking tool for machine learning. It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.

Google AutoML Tables and Neptune can be primarily classified 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, Neptune provides the following key features:

Experiment tracking
Experiment versioning
Experiment comparison

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Detailed Comparison

Google AutoML Tables	Neptune
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 brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.
Increases model quality; Easy to build models; Easy to deploy; Flexible user options; Doesn’t require a large annual licensing fee	Experiment tracking; Experiment versioning; Experiment comparison; Experiment monitoring; Experiment sharing; Notebook versioning
Statistics
Stacks 23	Stacks 16
Followers 64	Followers 38
Votes 0	Votes 2
Pros & Cons
No community feedback yet	Pros 1 Supports both gremlin and openCypher query languages 1 Aws managed services Cons 1 Doesn't have much community support 1 Doesn't have proper clients for different lanuages 1 Doesn't have much support for openCypher clients
Integrations
Google App Engine Google Cloud Dataflow	PyTorch Keras R Language MLflow Matplotlib

What are some alternatives to Google AutoML Tables, Neptune?

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.