Amazon QuickSight vs. Azure Machine Learning

Amazon Quicksight is a truly cloud-based solution so it works perfectly fine and saves a lot of expense in terms of hardware and maintenance. We can maintain it by ourselves by giving commands on UI. If you have connectivity issues then it can cause headaches because it's a cloud platform and it's a bit costly as compared to other services

Incentivized

For [a] data scientist require[d] to build a machine learning model, so he/she didn't worry about infrastructure to maintain it.

All kind of feature[s] such as train, build, deploy and monitor the machine learning model available in a single suite.

If someone has [their] own environment for ML studio, so there [it would] not [be] useful for them.

• Easily to set up for data sources, already supports quite a few of AWS and non-AWS data sources
• Cost friendly since users are charged only for basis of usage

Incentivized

• User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
• Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
• Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
• Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
• Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.

Incentivized

• It is still immature as a cloud-based BI tool.
• Its functionality is about 40-50% of its competitor's products.
• Application is still a little buggy and non-intuitive at times.

Incentivized

• It would be great to have text tips that could ease new users to the platform, especially if an error shows up
• Scenario-based documentation
• Pre-processing of modules that had been previously run. Sometimes they need to be re-run for no apparent reason

Incentivized

It is easy to use and set up no need to put in so much effort. Once build, the dashboard can be used with multiple clients with the same domain. It provides multiple connectivity options which makes it a versatile option for reporting.

Incentivized

Easy and fastest way to develop, test, deploy and monitor the machine learning model.

- Easy to load the data set

-Drag and drop the process of the Machine learning life cycle.

They provide proper support when needed. They are always ready to provide the box solution and make things easier for users.

Incentivized

Support is nonexistent. It's very frustrating to try and find someone to actually talk to. The robot chatbots are just not well trained.

Incentivized

Not sure

All of the other reporting platforms my organization has used previously were within our CRM and not a standalone program. In that we were very limited in being able to slice and dice the data the way that we wanted to

Incentivized

It is easier to learn, it has a very cost effective license for use, it has native build and created for Azure cloud services, and that makes it perfect when compared against the alternatives. As a Microsoft tool, it has been built to contain many visual features and improved usability even for non-specialist users.

Incentivized

• Reduce lots of setup and maintenance cost.
• Latest technology in market.
• Full eco system provided under on roof.
• Cost effective.

Incentivized

• Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
• Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
• Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details

Incentivized