Likelihood to Recommend A well-suited scenario for using AWS Tensor Flow is when having a project with a geographically dispersed team, a client overseas and large data to use for training. AWS Tensor Flow is less appropriate when working for clients in regions where it hasn't been allowed yet for use. Since smaller clients are in regions where AWS Tensor Flow hasn't been allowed for use, and those clients traditionally don't have enough hardware, this situation deters a wider use of the tool.
Read full review 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.
Read full review Pros Amazon Elastic Compute Cloud (EC2) allows resizable compute capacity in the cloud, providing the necessary elasticity to provide services for both, small and medium-sized businesses. Tensor Flow allows us to train our models much faster than in our on-premise equipment. Most of the pre-trained models are easy to adapt to our clients' needs. Read full review 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 Read full review Cons SageMaker isn't available in all regions. This is complicated for some clients overseas. For larger instances, when using a GPU, it takes a while to talk to a customer service representative to ask for a limit increase. Given this, it's recommendable to ask in advance for a limit increase in more expensive and larger cases; otherwise, SageMaker will set the limit to zero by default. Since the data has to be stored in S3 and copied to training, it doesn't allow to test and debug locally. Therefore, we have to wait a lot to check everything after every trail. Read full review 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 Read full review Likelihood to Renew 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
Read full review Usability 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
Read full review Support Rating 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.
Read full review In-Person Training The instructor was very knowledgeable.
Read full review Online Training The online instructor was very knowledgeable.
Read full review Implementation Rating Hire Qlik consultants. It's better done by them or with their aid.
Read full review Alternatives Considered Microsoft Azure is better than Amazon Tensor Flow because it provides easier and pre-built capabilities such as Anomaly Detection, Recommendation, and Ranking. AWS is better than IBM Watson ML Studio because it has direct and prebuilt clustering capabilities AWS, like IBM Watson ML Studio, has powerful built-in algorithms, providing a stronger platform when comparing it with MS Azure ML Services and Google ML Engine.
Read full review 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.
Read full review Return on Investment Positive: It has allowed us to work with our overseas teams without any large hardware investing. Positive: Pre-trained models significantly reduce the time to develop solutions for our clients. Negative: Since it's a relatively new tool, you have to be careful about not paying for large errors while learning to use the tool. Read full review 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. Read full review ScreenShots