Amazon Forecast vs. Pytorch

Amazon Forecast is well suited when you are a company that's looking for a simple and effective solution in terms of understanding and predicting your resources planning in the AWS. However, it's also good to know that the cost that is incurred is higher and not suited for anything other than the AWS solutions integrations.

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

• Built-in datasets
• Accuracy
• Machine learning

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

• Not easy for beginners as it requires insights to set the forecast
• Much more expensive if considered for small businesses

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

Cost-effective and user-friendly.

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

• Helped in planning the resources required.
• ML and AI delivered near perfect forecasts.
• Features and the details available for the cost is not enough.

Incentivized

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

Incentivized