StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. .NET for Apache Spark vs ml5.js

.NET for Apache Spark vs ml5.js

OverviewComparisonAlternatives

Overview

ml5.js
ml5.js
Stacks5
Followers53
Votes0
GitHub Stars6.6K
Forks908
.NET for Apache Spark
.NET for Apache Spark
Stacks31
Followers46
Votes0
GitHub Stars2.1K
Forks329

.NET for Apache Spark vs ml5.js: What are the differences?

Developers describe .NET for Apache Spark as "Makes Apache Spark™ Easily Accessible to .NET Developers". With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. On the other hand, ml5.js is detailed as "Friendly machine learning for the web". ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.

.NET for Apache Spark and ml5.js belong to "Machine Learning Tools" category of the tech stack.

.NET for Apache Spark and ml5.js are both open source tools. It seems that ml5.js with 2.73K GitHub stars and 212 forks on GitHub has more adoption than .NET for Apache Spark with 1.11K GitHub stars and 108 GitHub forks.

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)
or
Manual

Detailed Comparison

ml5.js
ml5.js
.NET for Apache Spark
.NET for Apache Spark

ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.

With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data.

Pre-trained models for detecting human poses, generating text, styling an image with another, composing music, pitch detection, and common English language word relationships; API for training new models based on pre-trained ones as well as training from custom user data from scratch
-
Statistics
GitHub Stars
6.6K
GitHub Stars
2.1K
GitHub Forks
908
GitHub Forks
329
Stacks
5
Stacks
31
Followers
53
Followers
46
Votes
0
Votes
0
Integrations
No integrations available
Apache Spark
Apache Spark
.NET
.NET
F#
F#
C#
C#
Ubuntu
Ubuntu

What are some alternatives to ml5.js, .NET for Apache Spark?

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.

Related Comparisons

Postman
Swagger UI

Postman vs Swagger UI

Mapbox
Google Maps

Google Maps vs Mapbox

Mapbox
Leaflet

Leaflet vs Mapbox vs OpenLayers

Twilio SendGrid
Mailgun

Mailgun vs Mandrill vs SendGrid

Runscope
Postman

Paw vs Postman vs Runscope