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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Aquarium vs Swift AI

Aquarium vs Swift AI

OverviewComparisonAlternatives

Overview

Swift AI
Swift AI
Stacks14
Followers52
Votes0
Aquarium
Aquarium
Stacks9
Followers11
Votes0

Swift AI vs Aquarium: What are the differences?

Developers describe Swift AI 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!. On the other hand, Aquarium is detailed as "*Improve Your ML Dataset Quality *". Machine learning models are only as good as the datasets they're trained on It helps ML teams make better models by improving their dataset quality..

Swift AI and Aquarium can be primarily classified as "Machine Learning" tools.

Swift AI is an open source tool with 5.74K GitHub stars and 552 GitHub forks. Here's a link to Swift AI's open source repository on GitHub.

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Detailed Comparison

Swift AI
Swift AI
Aquarium
Aquarium

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!

Machine learning models are only as good as the datasets they're trained on. It helps ML teams make better models by improving their dataset quality.

Feed-Forward Neural Network; Fast Matrix Library
Upload your dataset to get a health check of its quality, quantity, and diversity. Zoom in and out of your dataset. Uncover distribution biases before you train. Find and fix labeling errors quickly; Upload model inferences against your labeled datasets and deep dive into its performance. Find where your model is performing well and badly so you can take the best actions to improve it; With knowledge of your dataset diversity and model performance, it automatically samples the best data to sample to label and retrain on. Your model performance just gets better
Statistics
Stacks
14
Stacks
9
Followers
52
Followers
11
Votes
0
Votes
0
Integrations
Swift
Swift
No integrations available

What are some alternatives to Swift AI, Aquarium?

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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