Onfleet vs Swift AI

Overview

Onfleet

Stacks12

Followers18

Votes0

Swift AI

Stacks14

Followers52

Votes0

Onfleet vs Swift AI: What are the differences?

Developers describe Onfleet as "The easiest way for businesses to manage and analyze their local delivery operations". Onfleet transforms cumbersome last-mile logistics into delightful delivery management. Our intuitive smartphone apps, powerful web dashboard, and seamless customer notifications handle the heavy lifting so you can focus on what matters. On the other hand, Swift AI is detailed as "A.I. and machine learning library written in Swift". Swift AI is a high-performance AI and machine learning library written entirely in Swift. We currently support iOS and OS X, with support for more platforms coming soon!.

Onfleet belongs to "On-Demand as a Service" category of the tech stack, while Swift AI can be primarily classified under "Machine Learning Tools".

Swift AI is an open source tool with 5.58K GitHub stars and 542 GitHub forks. Here's a link to Swift AI's open source repository on GitHub.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Detailed Comparison

Onfleet	Swift AI
Onfleet transforms cumbersome last-mile logistics into delightful delivery management. Our intuitive smartphone apps, powerful web dashboard, and seamless customer notifications handle the heavy lifting so you can focus on what matters.	Swift AI is a high-performance AI and machine learning library written entirely in Swift. We currently support iOS and OS X, with support for more platforms coming soon!
Management dashboard;Driver app;SMS for customers;Comprehensive reports;Customer solutions via API	Feed-Forward Neural Network; Fast Matrix Library
Statistics
Stacks 12	Stacks 14
Followers 18	Followers 52
Votes 0	Votes 0
Integrations
Zapier	Swift

What are some alternatives to Onfleet, Swift AI?

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.

Related Comparisons

Onfleet vs Swift AI