Azure AI Language (formerly Azure Cognitive Service for Language) is a managed service to add natural language capabilities, from sentiment analysis and entity extraction to automated question answering. Users can identify key terms and phrases, understand sentiments, and build conversational interfaces into applications. Annotate, train, evaluate, and deploy customizable models without machine-learning expertise.
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TensorFlow
Score 7.7 out of 10
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TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
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
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
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
Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
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.
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
