Amazon Forecast vs. Caffe2 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

• flexibility
• Clean code, close to the algorithm.
• Fast
• Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
• Versatile, can work efficiently on text/audio/image/tabular datasets.

Incentivized

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

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• Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to Tensorflow

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The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.

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Cost-effective and user-friendly.

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Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems. Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.

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.

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• The ability to make models as never before
• Being able to control the bias of models was not done before the arrival of Pytorch in our company

Incentivized