Overview

Google AutoML Tables

Stacks23

Followers64

Votes0

AutoGluon

Stacks8

Followers38

Votes0

AutoGluon vs Google AutoML Tables: What are the differences?

Introduction

AutoGluon and Google AutoML Tables are both powerful machine learning tools that aim to simplify the process of building and deploying machine learning models. However, there are several key differences between the two. In this article, we will explore these differences in detail.

Pricing Model: AutoGluon follows a pay-as-you-go pricing model, where users pay for the resources they consume. On the other hand, Google AutoML Tables has a fixed pricing model based on the number of training hours and prediction requests made.
Deployment Options: AutoGluon allows users to deploy models on a variety of platforms, including on-premises servers, cloud platforms, and edge devices. In contrast, Google AutoML Tables only supports deployment on the Google Cloud Platform.
Customizability: AutoGluon provides a high degree of customizability, allowing users to fine-tune various hyperparameters and select different algorithms to train their models. Google AutoML Tables, on the other hand, offers a more simplified and user-friendly interface, with limited options for advanced customization.
Feature Engineering: AutoGluon offers automated feature engineering capabilities, which can save time and effort by automatically selecting and transforming relevant features from the input data. Google AutoML Tables, however, requires users to manually perform feature engineering tasks before training the model.
Supported Algorithms: AutoGluon supports a wide range of machine learning algorithms, including both traditional algorithms and neural network-based models. Google AutoML Tables, on the other hand, focuses more on automated machine learning and mainly uses pre-built models without exposing the underlying algorithms.
Model Interpretability: AutoGluon provides various tools and techniques for interpreting and explaining the decisions made by the trained models. Google AutoML Tables, in comparison, provides limited options for model interpretability.

In Summary, AutoGluon offers a more flexible and customizable experience with advanced features such as automated feature engineering and model interpretability. On the other hand, Google AutoML Tables provides a simpler and more user-friendly interface with a focus on automated machine learning.

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Detailed Comparison

Google AutoML Tables	AutoGluon
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 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.
Increases model quality; Easy to build models; Easy to deploy; Flexible user options; Doesn’t require a large annual licensing fee	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 23	Stacks 8
Followers 64	Followers 38
Votes 0	Votes 0
Integrations
Google App Engine Google Cloud Dataflow	Python Linux

What are some alternatives to Google AutoML Tables, 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.