Cube vs. TensorFlow

1) The budget process. In QBO the budgeting capability is non-existant, unless you like manually typing in every scenario and not being able to budget by class. Cube houses my budget/forecast scenarios & lets me view and analyze by my company's preferred data points; department, GL account, vendor, & sales campaign. I'm able to run monthly budget variance reports and plan for the future with ease. 2) We've begun using Cube to help analyze profitablity by sales job. We've never had such easy access to this type of info in the past, so this is a benefit I can directly attribute to Cube. 3) We're beginning now to use an integration with our payroll software to work on headcount planning and payroll analysis.

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).

Incentivized

• Push/pull information across any Google Sheet or Excel workbook.
• Budget at a much more granular level (by month by account by the department by vendor).
• Custom mapping to allow for multiple cuts of the same data.
• Bulk range selection within a workbook.

• 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.

Incentivized

• Limited to 8 top line dimensions. Although you can bring in as many attributes of data as you want, but I would really like Cube to increase top line dimensions to 10.
• The ability for cross level interaction within multiples cube would be a major plus once implemented.

• 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.

Incentivized

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.

Incentivized

Use of cloud for better execution power is recommended.

Incentivized

Cube was just a lot easier to use than Vena. We took some time to look at Vena as well and while their product was impressive, our organization was not yet there. We needed something we could implement quickly, and in today's day and age I think that is a very important quality to have. Start up and early stage companies do not have the luxury of implementation teams and massive IT resources so Cube was a huge help.

Incentivized

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

Incentivized

• It has decreased the amount of time it takes to build reports by days
• I can rest easy knowing the knowledge is accurate and there is no human error
• I have been able to create more reports than I ever dreamed of with the new time and ease of use

Incentivized

• 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.

Incentivized