Azure Machine Learning vs. Microsoft Azure

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.

Azure is particularly well suited for enterprise environments with existing Microsoft investments, those that require robust compliance features, and organizations that need hybrid cloud capabilities that bridge on-premises and cloud infrastructure. In my opinion, Azure is less appropriate for cost-sensitive startups or small businesses without dedicated cloud expertise and scenarios requiring edge computing use cases with limited connectivity. Azure offers comprehensive solutions for most business needs but can feel like there is a higher learning curve than other cloud-based providers, depending on the product and use case.

• 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

• Microsoft Azure is highly scalable and flexible. You can quickly scale up or down additional resources and computing power.
• You have no longer upfront investments for hardware. You only pay for the use of your computing power, storage space, or services.
• The uptime that can be achieved and guaranteed is very important for our company. This includes the rapid maintenance for security updates that are mostly carried out by Microsoft.
• The wide range of capabilities of services that are possible in Microsoft Azure. You can practically put or create anything in Microsoft Azure.

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

• The cost of resources is difficult to determine, technical documentation is frequently out of date, and documentation and mapping capabilities are lacking.
• The documentation needs to be improved, and some advanced configuration options require research and experimentation.
• Microsoft's licensing scheme is too complex for the average user, and Azure SQL syntax is too different from traditional SQL.

Moving to Azure was and still is an organizational strategy and not simply changing vendors. Our product roadmap revolved around Azure as we are in the business of humanitarian relief and Azure and Microsoft play an important part in quickly and efficiently serving all of the world. Migration and investment in Azure should be considered as an overall strategy of an organization and communicated companywide.

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.

As Microsoft Azure is [doing a] really good with PaaS. The need of a market is to have [a] combo of PaaS and IaaS. While AWS is making [an] exceptionally well blend of both of them, Azure needs to work more on DevOps and Automation stuff. Apart from that, I would recommend Azure as a great platform for cloud services as scale.

Incentivized

It has proven to be unreliable in our production environment and services become unavailable without proper notification to system administrators

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

We were running Windows Server and Active Directory, so [Microsoft] Azure was a seamless transition. We ran into a few, if any support issues, however, the availability of Microsoft Azure's support team was more than willing and able to guide us through the process. They even proposed solutions to issues we had not even thought of!

Incentivized

Not sure

As I have mentioned before the issue with my Oracle Mismatch Version issues that have put a delay on moving one of my platforms will justify my 7 rating.

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

As I continue to evaluate the "big three" cloud providers for our clients, I make the following distinctions, though this gap continues to close. AWS is more granular, and inherently powerful in the configuration options compared to [Microsoft] Azure. It is a "developer" platform for cloud. However, Azure PowerShell is helping close this gap. Google Cloud is the leading containerization platform, largely thanks to it building kubernetes from the ground up. Azure containerization is getting better at having the same storage/deployment options.

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

• For about 2 years we didn't have to do anything with our production VMs, the system ran without a hitch, which meant our engineers could focus on features rather than infrastructure.
• DNS management was very easy in Azure, which made it easy to upgrade our cluster with zero downtime.
• Azure Web UI was easy to work with and navigate, which meant our senior engineers and DevOps team could work with Azure without formal training.

Incentivized