Gluon vs cnvrg.io

Gluon vs cnvrg.io: What are the differences?

Developers describe Gluon as "Deep Learning API from AWS and Microsoft". A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components. On the other hand, cnvrg.io is detailed as "An end-to-end machine learning platform to build and deploy AI models at scale". It is an AI OS, transforming the way enterprises manage, scale and accelerate AI and data science development from research to production. The code-first platform is built by data scientists, for data scientists and offers unrivaled flexibility to run on-premise or cloud.

Gluon and cnvrg.io belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Gluon are:

• Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.
• Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.
• Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.

On the other hand, cnvrg.io provides the following key features:

• Machine Learning Pipelines
• AI Library
• Open Compute

Gluon is an open source tool with 2.32K GitHub stars and 230 GitHub forks. Here's a link to Gluon's open source repository on GitHub.