Amazon Tensor Flow vs. Sisense

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.

Incentivized

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.

Incentivized

• 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.

Incentivized

• 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.

Incentivized

• 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.

Incentivized

• 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.

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

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.

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.

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.

Incentivized

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.

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

• 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.

Incentivized

• 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