Sisense vs. TensorFlow

I believe Sisense is perfectly suited for any organization of any size that have access to the proper resources, as the tool is very expensive. The data connectors come in all shapes and sizes out of the box, which allows a great deal of data control within the ElastiCubes. Additionally, while the platform only runs on Windows platforms, the web application can be accessed on any client: mobile, Apple, Windows, etc. This allows a much more flexible user experience, resulting in data and dashboards reaching further than any other tool.

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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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• Customization - lots of supported and community plugins available. Users can also adjust the look and feel through JavaScript.
• Innovation - Sisense is continuing to push the envelope by developing new features through Sisense Labs.
• Customer support - the commitment to excellent customer service and ensuring organizations get the most out of their investment.

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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 usability of the application on mobile devices needs some improvement, especially navigation and filtering.
• Dashboards that are created by multiple users can be a bit of a hassle to share by Admins.
• If you need to embed dashboards into your website, you are require to buy a license separate from the user and platform license. This is a norm on most BI visualization tools, but Sisense can seem a bit on the high side, cost-wide.

• 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 think the business and myself as a user has come to rely on SiSense as a dashboarding and quick ad-hoc reporting tool. I am hoping to integrate SiSense dashboards into more parts of the business in the future. We have reduced our report turn-around time for the most part from hours/days to minutes and in some cases almost the speed of thought. Reports are also easier on the eye and more easily distributed. I would also like to say that the support and professionalism from the SiSense team has been excellent.

New V5 is ground floor of an exciting collection of possibilities. Weekly Sisense developers come up with new functionality that they share with us in their forums. The move to HTML5 has been pleasing in that widgets auto size themselves into appropriate forms in the board but everyone of them can be popped out to full page size to be looked at in more detail

Support of multiple components and ease of development.

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There are very few situations when there is unexpected downtime. Mostly during development, new dashboard implementation and during upgrades. other then that there were very few crashes.

SiSense is usually performing better then other solutions even if going for complex reports/dashboards(of course within reasonable frames). I haven't noticed any bad influence on other systems, usually if something happens it stays within SiSense.

SiSense's support ninjas are very knowledgeable and are exceptionally responsive. So far, all of the issues we ran into were resolved within minimum time. My sense of dealing with the support staff at SiSense is that they are very focused on not just answering your immediate question, but also to delve into the cause of the matter.

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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Easy and free training that allowed us quickly understand basics in SiSense and start using them. More advanced features requires some browsing through SiSense forums, but there is always support to help, and SiSense support is one of the best whith which I worked so far.

Many examples, videos and scenarios which you try on your own right away. This combined with in-person training gives you enough to utilize most of SiSense's power.

Remember to migrate the plugins. The documentation however is pretty straight forward.

Use of cloud for better execution power is recommended.

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1) Easy to use, really, there is nothing too much to say. The set up is easy and not confusing. You can use it internally or externally.

2) Customer Service, having spoken to various product reps from similar industry. Sisense rep provides you with the best support to get started, and it is really appreciated.

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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Product is quite easily scalable. The whole installation can be easily moved to stronger machine if needed.

• Report development time is reduced from 2~3 days to 2~3 hours.
• Report querying time is improved from 0.5~10 mins to 2~30 seconds.
• A 2-staff team delivers 10 times the BI output that we did on the old system.
• We were able to deliver BI and data analysis for all business functions instead of a very limited group of users

• 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