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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. AWS DeepRacer vs Cortex.dev

AWS DeepRacer vs Cortex.dev

OverviewComparisonAlternatives

Overview

Cortex.dev
Cortex.dev
Stacks7
Followers19
Votes0
GitHub Stars8.0K
Forks604
AWS DeepRacer
AWS DeepRacer
Stacks3
Followers6
Votes0

Cortex.dev vs AWS DeepRacer: What are the differences?

Cortex.dev: Deploy machine learning models in production. It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command; AWS DeepRacer: The fastest way to get rolling with machine learning. Developers of all skill levels can get hands on with machine learning through a cloud based 3D racing simulator, fully autonomous 1/18th scale race car driven by reinforcement learning, and global racing league.

Cortex.dev and AWS DeepRacer can be primarily classified as "Machine Learning" tools.

Some of the features offered by Cortex.dev are:

  • Autoscaling
  • Supports TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, and more
  • CPU / GPU support

On the other hand, AWS DeepRacer provides the following key features:

  • A fun way to learn machine learning
  • Master the basics with time-trial racing
  • Expand your skills with head-to-head racing

Cortex.dev is an open source tool with 5.71K GitHub stars and 444 GitHub forks. Here's a link to Cortex.dev's open source repository on GitHub.

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Detailed Comparison

Cortex.dev
Cortex.dev
AWS DeepRacer
AWS DeepRacer

It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

Developers of all skill levels can get hands on with machine learning through a cloud based 3D racing simulator, fully autonomous 1/18th scale race car driven by reinforcement learning, and global racing league.

Autoscaling; Supports TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, and more; CPU / GPU support; Rolling updates; Log streaming; Prediction monitoring; Minimal declarative configuration
A fun way to learn machine learning; Master the basics with time-trial racing; Expand your skills with head-to-head racing
Statistics
GitHub Stars
8.0K
GitHub Stars
-
GitHub Forks
604
GitHub Forks
-
Stacks
7
Stacks
3
Followers
19
Followers
6
Votes
0
Votes
0
Integrations
TensorFlow
TensorFlow
PyTorch
PyTorch
XGBoost
XGBoost
scikit-learn
scikit-learn
Keras
Keras
No integrations available

What are some alternatives to Cortex.dev, AWS DeepRacer?

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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