Qlik Cloud Analytics (Qlik Sense) vs. TensorFlow

Qlik Sense is a program whose purpose is to greatly improve all your operations and use of all data in an organic way. The mission will always be to increase the economic and commercial processes of the company in a short time. I recommended it for its high technology, which was Created for this area, the results are successful. We have noticed how it has increased relationships with our clients thanks to the credibility and security that we provide.

Incentivized

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

• Flexibility in data recording, from the app, as well as in handling them. Allowing you to view reports quickly and effectively.
• Easy to generate dynamic graphics according to our needs.
• It is very easy to use, intuitive, so it does not generate costs in the learning process compared to the tool with collaborators

Incentivized

• 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

• Scalability is RAM limited, all data is associative and in-memory so server's RAM is directly proportional to performance
• Limitation on advanced analytics capabilities in the form of R integration to perform complex statistical and analytical calculations
• Can have more intuitive interface

Incentivized

• 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.

Qlik Sense is a constantly improving it's software and working with its' users to make it better. They are great at keeping their users informed of progress and care about delivering a quality product

Incentivized

Qlik Sense has a better and easy to learn user interface compared with other analytics tool which always help us to create regular and adhoc reports within the stipulated time frame and can be easily refreshed at a scheduled time and sent to multiple stakeholders for timely update regarding the Key metrics indicator.

Incentivized

Support of multiple components and ease of development.

Incentivized

Qlik is great for companies with lots of business domains and departments because it scales well, especially if data that is reported is saved in SQL and similar structures. Its ease of use and good UI enables business units to create and manage their own reports. That removes a great burden of creating and managing/modifying these pages from the IT team. Overall, it's a win-win for both IT and business units.

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

The instructor was very knowledgeable.

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The online instructor was very knowledgeable.

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Hire Qlik consultants. It's better done by them or with their aid.

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Use of cloud for better execution power is recommended.

Incentivized

The customization of the platform opens up plenty of other options depending on the use cases. The API layer is incredibly rich and makes integration of Qlik based visualization into web pages a simple and effective pattern. It's been very easy to use with a great community made up of professionals. Qlik Sense has introduces artificial Intelligence into my data visualization and reporting activity.

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

• Once an app is published and running good, we can quickly see the time we save to analyze data.
• It's hard sometimes to realize the money we save by using this system.
• People start understanding the power of data and asking more questions.

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