Magento vs TensorFlow: What are the differences?
What is Magento? Flexible eCommerce solutions, a vibrant extensions marketplace and an open global ecosystem. Magento Community Edition is perfect if you’re a developer who wants to build your own solution with flexible eCommerce technology. You can modify the core code and add a wide variety of features and functionality.
What is TensorFlow? Open Source Software Library for Machine Intelligence. 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.
Magento can be classified as a tool in the "Ecommerce" category, while TensorFlow is grouped under "Machine Learning Tools".
"Open source" is the primary reason why developers consider Magento over the competitors, whereas "High Performance" was stated as the key factor in picking TensorFlow.
Magento is an open source tool with 7.52K GitHub stars and 6.43K GitHub forks. Here's a link to Magento's open source repository on GitHub.
According to the StackShare community, TensorFlow has a broader approval, being mentioned in 195 company stacks & 126 developers stacks; compared to Magento, which is listed in 147 company stacks and 48 developer stacks.
What is Magento?
What is TensorFlow?
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Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
We offer our integration services for Magento 2.0 as well. We integrate Magento with third party applications using the REST protocol and help make sales cycle short an efficient.
Machine Learning in EECS 445