Azure AI Vision vs. Pytorch

It offers a great option for IoT and simple repetitive tasks with simple configuration but it requires having previous knowledge of Microsoft tools to get familiar with how to use it. I also think it is excellent for classification especially if your requirements about specific tools could need a lot of studies.

Incentivized

They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.

Incentivized

• Out of the box capabilities.
• Options of analysis.

Incentivized

• Provides Benchmark datasets to test your custom algorithm
• Provides with a lot of pre-coded neural net components to use for your flow
• Gives a framework to write really abstract code.

Incentivized

• Material to learn.

Incentivized

• Distributed data parallel still seems to be complicated
• Support for easy deployment to servers
• Torchvision to have support for latest models with pertained weights

Incentivized

As I described in previous statements, Pytorch is much better suited than TensorFlow from a software development look. This Pythonic idea was then taken and repeated by all the other frameworks. You can get to better performance models by better understanding the deep learning model code, so I think the choice of Pytorch is easy and simple.

Incentivized

• It provided easy configuration for project requirements.
• Allow me to execute simple tests.
• Great manage of resources.

Incentivized

• I'd estimate I can build a model 50% faster on pytorch vs other frameworks

Incentivized