MLReef vs. Pytorch

Works well if you have to involve different roles in different organizations in a project. Less suited when you have a complex system of custom developed tools

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

• Helps us to take on more client projects
• Can be used by data analysts as well as casual users

• 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

• Out of the box support for major cloud vendors

• 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

• We can handle 4 to 6 times more projects at the same time with our team
• We stay engaged with our customers well beyond the project duration

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

Incentivized