SAS Visual Analytics vs. TensorFlow

I was in a meeting with the client and there I have to show them some analytic data to them. But I was confused about how I will manage to show big data to clients with accuracy. But then the SAS Visual Analytics software helps me in presenting accurate data at the moment and it was very presentable and through that, I got the deal for that business.

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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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• Provides the flexibility to the end user to slice and dice the data.
• Anyone can make predictive models with the help of in-built algorithms without the need to write a single line of code or knowledge of what's under the hood of algorithms.
• The feature to simply ask a question related to data and getting a response in form of text, chart or graph is amazing.

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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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• SAS is relatively expensive when compared to other BI tools and requires a large amount of upfront fee which becomes an issue for smaller organizations.
• UI for the dashboards looks a little date in comparison to competitors like Tableau and Microstrategy.
• Integration with other open source software like Python needs to be built in.

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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.

SAS really is the cutting edge in Business Intelligence. That is all they do! They are constantly coming out with new products, product upgrades, and their tech support is second to none. In addition, their support of Education has made our ability to acquire their product possible.

SAS BI is good for creating reports and dashboards and then sharing it with the users. It also has ability to manage access to the reports and dashboards but somehow with most of the world moving to open source languages R, Python and Julia, SAS BI feels to be archaic in terms of feature set and integrations it allow[s]. Also, comparing it with other Business Intelligence tools like Tableau and Microsoft BI, the functionality of SAS BI is very limited and doesn't justify the pricing.

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Support of multiple components and ease of development.

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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

When you call tech support, you are immediately routed to a person who can answer your question. Often they can answer on the spot. However, if they cannot, you are given a track number and then followed up with. There have been times when I have had multiple track numbers open and they will actually TRACK YOU DOWN to ensure that your problem has been resolved. Issues do not fall into black holes with SAS. They are also willing to do a WebEx with you to diagnose the problem by seeing your environment, which is always helpful.

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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I have used Crystal Reports, Jaspersoft and SQL Server Reporting Services (SSRS). I would recommended Business Intelligence over SSRS and Crystal Reports. SSRS is very SQL-centric and Crystal Reports is more of an end-user tool. I would recommend Jaspersoft over Business Intelligence for developing a seamless web-based reporting interface but I highly recommend Business Intelligence for end-user ad-hoc reporting.

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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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• SAS Visual Analytics licensing cost should be reduced to enable users to use it in mass scale.
• More statistical and mathematical equations and theories should be converted into SAS functions for reusing.
• Because of enriched ETL and reporting capabilities SAS Visual Analytics is most preferred by business users.

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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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Independently Invited by TrustRadius

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