Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
$0
per month
Node.js
Score 9.9 out of 10
N/A
Node.js is an open source, asynchronous event-driven JavaScript runtime, Node.js is designed to build scalable network applications.
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.
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.
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.
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.
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
