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  5. AWS DeepRacer vs sktime

AWS DeepRacer vs sktime

OverviewComparisonAlternatives

Overview

AWS DeepRacer
AWS DeepRacer
Stacks3
Followers6
Votes0
sktime
sktime
Stacks7
Followers15
Votes0

sktime vs AWS DeepRacer: What are the differences?

sktime: A unified toolbox for machine learning with time series (by The Alan Turing Institute). It is a Python machine learning toolbox for time series with a unified interface for multiple learning tasks. It provides dedicated time series algorithms and scikit-learn compatible tools for building, tuning, and evaluating composite models; 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.

sktime and AWS DeepRacer can be categorized as "Machine Learning" tools.

Some of the features offered by sktime are:

  • Forecasting
  • Time series classification
  • Time series regression

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

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Detailed Comparison

AWS DeepRacer
AWS DeepRacer
sktime
sktime

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.

It is a Python machine learning toolbox for time series with a unified interface for multiple learning tasks. It provides dedicated time series algorithms and scikit-learn compatible tools for building, tuning, and evaluating composite models.

A fun way to learn machine learning; Master the basics with time-trial racing; Expand your skills with head-to-head racing
Forecasting; Time series classification; Time series regression
Statistics
Stacks
3
Stacks
7
Followers
6
Followers
15
Votes
0
Votes
0
Integrations
No integrations available
Python
Python
scikit-learn
scikit-learn

What are some alternatives to AWS DeepRacer, sktime?

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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