Azure Machine Learning vs. IBM Cloud Functions

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.

IBM Cloud Functions [is] not the worse product on the IBM cloud. I decided to write this review as I thought it would be balanced. I would still use functions to set up a serverless architecture where execution time is pretty quick and the code is relatively simple. I wouldn't use IBM Cloud Functions for async calls obviously, as costs could be higher. The functions documentation is lacking in terms of CI/CD, and there are unexplainable errors occurring - like the network connection that I mentioned. So I wouldn't just rely on IBM Cloud Functions too much for the entire system, but make sure it's diversified.

• 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

• Great substitute for a simple API calls to run non-complicated code.
• Easy way to run Python/Java/Javascript to get something done.
• File validation.

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

• Billing can be a hassle, not the most responsive customer service/support team
• Handles & executes most functionalities, but other platforms offer more scalability if you're seeking consistent and stable growth

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

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

• ICF is a lightweight service and does not require runtime configurations
• Scalable on demand and hence there is no need to pay for runtime costs

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

• It directly affected our expenses since we do not need to deploy and maintain a set of separate applications.
• It allowed us to pay for only the amount of time cloud functions run.
• It saved on maintenance and monitoring of the applications it replaced.

Incentivized