Amazon Personalize vs. Azure Machine Learning

Well suited for vast business needs, Less suitable when planning for small limited business requirements. However, if the business is vast & the business requirements become vast, its easy to comprehend with Amazon Personalize, as most of the functions would be pre-built & as users, we can customize to our needs to meet business demands.

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.

• Ingest virtually unlimited quantities of transactional data.
• Provide world-class training capabilities.
• Provide a free-tier to start your project.

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

• Repetitive Recommendations.
• Needs to improve the search speed.
• Costly to purchase.

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

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.

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

Easy to integrate with existing applications, Easy to personalize for new users, Batch deploy, good recommendations and good suggestions based on current requirements, business goals get easy prioritized based on user needs and recommendations, it can work with existing tools and easily adapt the details and requirements from the existing tools.

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

• We managed to analyze huge amounts of data from transactional records.
• We produced 5 different user/profile segments for our marketing team.
• We learned how to power marketing predictions using machine learning.

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