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

1. Whenever the problem has the demand for a neural networks based solution, Tensorflow (TF) is a great fit.
2. The tf.dataset API makes it really simple to create complex data pipelines in a few lines of code.
3. tf.estimators API abstracts all the complex computation graph creation logic making it very simple to get started.
4. Eager execution makes it simple to develop a TF graph as debugging the code would be like any other imperative Python program.
5. TF abstracts all the complexities of scaling it to multiple machines. It has various code and data distribution algorithms ready to use.
6. Projects like TensorBoard make monitoring the training process really easy. It also gives the ability to view embeddings without any extra code. Their What-If is extremely useful for poking and understanding a black box model. It also has tools to visualize data to quickly check for anomalies.
7. TF Autograph aims to covert any normal Python code into a distributed program which is quite handy to scale an existing code base.

Incentivized

• Self-Service Visualization and Discovery
• Fullstack BI from ETL to Visualization, its built-in ETL engine can transform disparate data sets into a common data structure
• Uses HTML5 based responsive design which visualization automatically adapt to the screen size and orientation of the users device
• Actively engaged community and resources to help you learn this software in the best possible manner

Incentivized

• Data pipeline implementation is quite good, loading large amounts of data and pre-process it in an efficient way is no more issue for us
• It supports all major DL algorithms and network layouts such as ConvNets, RNN, LSTMs, Word2Vec, and even the latest transformer architecture
• The abstraction for the device is perfectly done and its support seamlessly for multiple GPU and even TPU will bring a lot of performance gain for enterprise scoped solution while still keep the flexibility
• The TensorBoard is amazing. I haven't seen a similar thing in other frameworks on the market. It allows us to quickly understand and debug the model with the info visualization which makes understanding much better
• A very supportive community, which is the key for sharing the ideas and find the quick and best solutions

Incentivized

• Not a lot of room for customization as we were used to in QlikView
• UI and default navigation can be very clunky and not user friendly
• Although the backend is fantastic, the front end experience leaves a lot to be desired. As a developer you don't have a lot of options to customized your app unless you turn to Javascript, CSS and HTML. This is not a common stack you would find in most BI developers

Incentivized

• It would be much better if they could provide good documentation and easy ways to understand concepts.
• It is difficult to understand the concept behind for example, Tensor Graph, which takes a lot of time.
• As you have to write everything, it is time consuming to write the implementation of whole neural network. It would be better if they can provide some wrapper library to make things easier.

Incentivized

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

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.

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.

Incentivized

The online instructor was very knowledgeable.

Incentivized

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

Incentivized

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.

Can't seem to choose any deep learning platform in the above, so I'll list it here: 1. Apache MXNet: this has been used for one of our main algorithms for search as an end-to-end pipeline. We chose this because of the Scala bindings, which makes it easier to integrate with out JVM backend. MXNet seems comparable to TensorFlow, although community support is not as good as TensorFlow, and there are issues with memory leaks that are being worked on. TensorFlow in general is easier to use, but MXNet isn't too far behind. 2. Keras: still a favorite. Often I use this when paired with TensorFlow. TensorFlow 2.0 will make it even easier. 3. PyTorch: only used it a little, so it's hard to provide a good opinion. 4. DL4J: used it initially in an early days project because it has good JVM support. Harder to used not because of poor API design, but because community support is lacking and features don't come out as fast as TensorFlow.

Incentivized

• The impact has undoubtedly been positive, it is difficult to quantify it, however in terms of effectiveness or efficiency I give it a 90%.
• I don't give it 100% because to use the complete package you have to pay, and it's not that cheap and on the other hand because it has some deficiencies, such as technical support and some issues like windows that are not so friendly and easy to work with.
• Still, I can't stop using it.

Incentivized

• Positive Impact- As I mentioned before its open source. Very easy to learn for average programmer/ developer. We were able to design a POC model for understanding the patient appointment cancellation snd reasons behind it in 3 week time frame.
• Negative Impact- If you are using tensor flow for small project it works fine. If you are trying to build a model for face recognition it will be hard to program and train the system. It needs data to be processed before hand cannot learn on the go.