Neptune vs AutoGluon

Overview

Neptune

Stacks16

Followers38

Votes2

AutoGluon

Stacks8

Followers38

Votes0

AutoGluon vs Neptune: What are the differences?

What is AutoGluon? *AutoML Toolkit for Deep Learning *. 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.

What is 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.

AutoGluon and Neptune can be primarily classified as "Machine Learning" tools.

Some of the features offered by AutoGluon are:

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

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

Experiment tracking
Experiment versioning
Experiment comparison

AutoGluon is an open source tool with 1.92K GitHub stars and 216 GitHub forks. Here's a link to AutoGluon's open source repository on GitHub.

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Detailed Comparison

Neptune	AutoGluon
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.	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.
Experiment tracking; Experiment versioning; Experiment comparison; Experiment monitoring; Experiment sharing; Notebook versioning	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 16	Stacks 8
Followers 38	Followers 38
Votes 2	Votes 0
Pros & Cons
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	No community feedback yet
Integrations
PyTorch Keras R Language MLflow Matplotlib	Python Linux

What are some alternatives to Neptune, 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.