Azure Machine Learning vs. Node.js vs. OpenText IDOL Unstructured Data Analytics

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.

1. Node.js is useful for building one page, fast, light-weight, scalable applications. 2. It is not suitable for building computationally extensive applications, it may lead to bad performance.

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

• Robust
• Fast
• Modern
• Easy to learn
• Allows using JS or TypeScript everywhere
• Supported and maintained actively by the open source community
• Pleasurable to work with
• Reliable
• Simple to learn
• Simple to setup

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 reduce performance while dealing with heavy computational task.
• Node.js invites a lot of change in code, due to an unstable api.
• Lack of library support.

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

Node.js is easy and good to use, their support team is also good they always tend to help you, and solve your problem, even we know that Node is free to use and opensource but then also we get support from them but for getting much better results we need to purchase standard or enterprises support provided by them.

Incentivized

Not sure

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

There is a smooth and seamless performance for multiple requests as the feedback received from users. and uses one platform to maintain UI and backend.

• 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

• It helps our business to expand our standard offer with new solutions based on Node.js such as conversation bots.
• It brings avarege 2-3 new clients projects per year, that we couldn't do without it.

Incentivized