Qlik Sense® is a self-service BI platform for data discovery and visualization. It supports a full range of analytics use cases—data governance, pixel-perfect reporting, and collaboration. Its Associative Engine indexes and connects relationships between data points for creating actionable insights.
$20
per month per user (10 user minimum)
TensorFlow
Score 8.9 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
N/A
Pricing
Qlik Sense
TensorFlow
Editions & Modules
Standard
$20
per month per user (10 user minimum)
Premium
$2700
per month unlimited basic users & purchased full users
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.
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).
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
Standard user interface and powerful analytic functions is GREAT. As technical person working in the background there are more things to do to make this a completely great tool. Some functions that should be standard requires consult scripting and hours. Now we are using it quite advanced and with many servers and in combination with QlikView. So overall I love the tool. But it could be better and user friendly and powerful in the background
Not only can you ask the support team for help, but you can also ask the community. Also with the community there is a vast amount of problems that have already been solved. The problem you are encountering has a likely chance of already being discussed and even solved in the community section saving you time from reaching out.
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.
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
