Azure AI Language vs. Pytorch

Best suited for large organizations, availability of usage of more than one language in a specific API call. Moderately suited for small and mid sized organizations as the pricing is on a higher end

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

• The data is pre configured that means the AI models that are used by features are not customizable. One needs to send data and have to use the output of the feature in our application
• Availability of customizations in order to adjust some specific requirements
• Availability of Language studio so that one can avoid coding

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

• The application is hard to use for new users
• Data Integration is complex in nature
• For Mid - sized organizations, the pricing is on higher end

Incentivized

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

Incentivized

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.

Incentivized

We haven't used other products. Our experience with Azure, whilst not meeting our needs this time around, was positive, educational and insightful. This was the first time that we had considered using an AI tool to manage and manipulate our data and we were unsure of the capabilities.

Incentivized

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

• Usage of more than one language in one specific API call
• Question Answering Feature
• Key Phrase Extraction, hence one need to read the entire phrase to understand the context

Incentivized

• 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