TaxJar vs. TensorFlow

If you aren't a tax professional, this is a must. Trying to manually calculate which county/city/state sales tax is owed will break your brain. Actually, even if you were a tax professional, why would you want to manually do it when you could just have a tool like this to calculate it for you. I wish it was also affordable to have it file for you and there are some crunchy bits, but its pros definitely outweigh the cons.

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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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• The CSV upload for clients that do not have or want to connect directly is very good. It allows us to get a lot of the information done quickly.
• The cost is very competitive.
• According to our clients, the integration is relatively easy.
• For the most part, the reports are accurate.

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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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• Continually increase its native integration features with BigCommerce, which recently just opened up more Checkout APIs and Sales Tax APIs.
• The Amazon integration is confusing only because Amazon (sometimes?) calculates and collects sales tax on your behalf. It's not clear what's best-- to let Amazon do it for you or for TaxJar to handle it.
• Onboarding could be a bit easier and guided.

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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.

Support of multiple components and ease of development.

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We had a few questions about integrations & onboarding, and support answered quickly and in-depth. They understood our questions - which were a bit technical in nature - and were able to provide an explanation in a timely and specific manner that allowed us to get the system up and running quickly.

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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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TaxJar is less expensive, simpler to integrate and their billing is straightforward so that you don't have to worry about huge surprises at the end of a month or quarter. They spend a lot less time on sales/marketing but their support is excellent and overall we have had a much better experience with TaxJar.

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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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• With regards to ROI, it definitely saves time. And time, as they say, is money. It may be hard to put a particular dollar sign on this, but it definitely has provided a positive return.
• It keeps me aware of where I have sales tax nexus that I might not know about otherwise. This is important. Even if I have to check it manually, at least I am made aware of where I need to file.
• It allows me to track how much in sales tax we've paid year over year, which is a good indication of how our sales are doing.

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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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