SnapEngage vs. TensorFlow

It helps business grow, if your business is more reliable on marketing or if your business is in starting stage implementing SnapEngage to your website will give a kick start to your business because it helps to get close with the customers which are in need with those quick questions and responses we are getting from customers using chat agent.

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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).

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• Allows team members to see type in real-time before it is submitted. This enables us to gather information for responses in a more timely manner.
• We can see which of our team members are available, busy, or not-available.
• Ability to see some biographical information of chatters and to sync information with Salesforce.

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• A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
• Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
• Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.

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• The admin dashboard is the hardest to navigate of any tool I've ever used! THere are 7 tabs in the left-hand panel. Just within the one tab that reads "Settings" (there is a separate tab for "My Account", also Permissions?) there are 9 tabs at the top (which include names like "Agent Settings", "Integrations", "Design Studio", "Options", "Hub"), then at least another 8 tabs WITHIN those 9 tabs, giving you a total of 14 different pages of settings to search through, again, JUST in the Settings tab. What the heck?!
• Something as simple as notification settings are spread throughout the 14 different settings pages mentioned above. Rather than having one area where you can enter email addresses for notifications, I've had to search through the 14 pages and use Ctrl+F on multiple occasions to fully remove a user from all the notifications they received. This should be much simpler!

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• RNNs are still a bit lacking, compared to Theano.
• Cannot handle sequence inputs
• 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.

I give SnapEngage a 9 due to how successful our company has been while using it. Unless prices were raised by an insane amount, I don't see us using a competing service.

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Support of multiple components and ease of development.

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I have never needed to contact them for support.

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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.

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Use of cloud for better execution power is recommended.

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We actually demo'ed LiveChat and LivePerson and besides SnapEngage having a better UI and ease of use, the support from their team was worlds and away above the rest. They let us run an extended demo, gave us constant support, and made sure we felt comfortable before we went live with the system.

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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

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• It has allowed us to provide more service with a much smaller team than our phone team.
• It provides us with data helpful to not only ourselves, but our marketing team as well.
• It's customizable so it allows us to create and update our platforms immediately when needed.

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• Learning is s bit difficult takes lot of time.
• Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
• Once you have learned this, it make your job very easy of getting the good result.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info