We never went into production because we switched to Azure Databricks. We did, however, try some performance testing and tried scaling some resources. The scalability of this solution is quite easy.

It is not difficult compared to some of the other tools that are available on Azure.

We have only five users including data engineers, data scientists, and one data DevOps engineer who was working with us on creating all of the DevOps pipelines for deploying all of our models.

We haven't faced any challenges with scalability. If there are any issues, our Microsoft infract team pitches in but we haven't had any serious problems. We have around 25 to 30 customers accessing this solution. Maintenance is straightforward and doesn't require more than one person. 

The solution is scalable. I'd rate it ten out of ten. 

We have 10 to 15 users as of now on the product.

We use it often. 

The solution can scale. I haven't used Azure Kubernetes services yet. However, I haven't had issues with scaling so far. 

We have around ten to 20 people on our project using the solution. Many users use it in our company - not just on my team.

It is a scalable solution. I do not know how many users are using the solution in my company since I am not from the administration department. So, maybe people from the administration department might know the number of users in our company.

I am not aware of how many technical staff members are needed for deployment and maintenance.

I would rate it eight out of ten.

The solution is really scalable.

I rate the solution a seven out of ten for scalability.

I would rate the scalability of Azure Machine Learning Studio at about a seven out of ten. While it offers high scalability, it can be challenging for less technical users and may encounter issues with defects and industrial licensing, particularly in logistics projects.

At our company, we use Azure Machine Learning Studio daily.

Scalability-wise, I rate the solution a seven out of ten. My company still has to do some of our own optimizations to the data part of the solution until and unless we subscribe to some third-party data lake services, which is a better option but comes at a higher cost.

My company's client's organization has around 10 to 50 users of the solution.

My company caters to the requirements of medium and enterprise-sized companies.

Microsoft Azure Machine Learning Studio is a solution that's easy to scale. It's pretty easy because it is hosted on Kubernetes, and there is an option in the portal where I can simply move my plan from standard to enterprise. The solution also has an automatic scaling option available because it is on Kubernetes, so it can scale automatically. I'm seeing that it's quite scalable. This has nothing to do with availability because it just runs in the background, and it is not customer-facing, but the output is customer-facing, so availability is a different case, but in terms of scalability, Microsoft Azure Machine Learning Studio is scalable.

I didn't have any issues with the scale. we rapidly went from test to full implementation across all datasets.

It is a scalable solution because it works well for large databases.

I would rate the scalability a nine out of ten. 

No issues with scalability.

Microsoft Azure Machine Learning Studio is a very scalable solution. Three people are using the solution in our organization.

I rate the solution an eight out of ten for scalability.

In terms of scalability, I believe that the solution is good. Although I have only used it for two projects, I think that it provides a good level of scalability. However, as I have only used it within my organization, I may not have experienced all of the possibilities that the solution offers.

It's quite scalable. It's on the cloud which makes it quite scalable.

We tend to use it for medium-sized organizations. The number of users is around 10 to 15. They are mostly engineers. 

Five people use the product in our organization. I rate the tool’s scalability a ten out of ten.

Microsoft Azure Machine Learning Studio itself is not really designed to be deployed. You get the model output from Machine Learning Studio, and then you have to use other Azure services for deployment. Thus, it's not very scalable in that sense.

However, for scalability in terms of machine learning and running different algorithms, I would rate it at eight out of ten. In terms of deploying machine learning solutions, I would not rate it very high. I am the only one who uses this solution in my organization, and we are not planning to increase usage at present.

Microsoft Azure Machine Learning Studio

The product is scalable, especially on-premises. It can be scaled as large as you need it to be. It is also good for multiple users and machine learning workloads. You can choose the payment plan that best suits your needs.

However, the level of data protection may be lower than if you were to use a platform specifically designed for SMBs.

Microsoft Azure Machine Learning Studio is a scalable tool. My previous company was on a volume-based model with it, and even if the data is large, it's easy to scale.

The solution is scalable.

I would rate its scalability capabilities nine out of ten. Ten users utilize it on a daily basis.

It's very scalable because it is using Microsoft cloud compute power.

We want to extend organization-wide, but currently, we are only working on a use case basis.

If a company needs to scale the solution, they should have no problem doing so. I don't see any aspect of the solution that would stop a user from expanding it as needed.

Currently, we only have a handful of users. There are only about five to seven people on the product right now.

We do plan to continue to use the product and to increase usage in the future.

Scalability for us was fine.

We have about seven hundred users including customer service agents, sales agents, and cell phone account managers. It took us about twelve months to scale to this point, from an initial user base of seventy people, and we do not plan to increase usage further.

The tool is scalable. We have four users in our organization. We have plans to increase the usage in the future.

It is a scalable product.

I have found Microsoft Azure Machine Learning Studio scalable.

We have approximately eight people using the solution in my organization.

From my experience, I think that it's scalable.

Scalability-wise, we have not had to deal with any limitations. The only problem is that when certain options are not there, we have to use Python or R to handle those tasks.

Scalability, in terms of running experiments concurrently: Good. At max, I was able to run three different experiments concurrently.

Scalability in terms of deploying models: Unknown, I never deployed on Azure.  But I would guess REST API could probably easily handle a few K worth of hits per second, since that is how Microsoft is going to get paid.

This solution is scalable.

The solution seems to be able to work well for companies both large and small. However, I did not personally attempt to scale it.

I don't have enough knowledge about the solution's scalability to be able to comment on it. Right now, we have about 5,000-6,000 users on the solution. Most are data scientists, and IT admins.

Azure ML Studio has the same scalability as other similar solutions.

I believe that it is scalable. At this time, we have not more than ten users. These include programmers, as well.

There are no scalability issues at the moment as data volume is still low.

The solution is extremely scalable. This is because it's on the cloud. If a company needs to scale up they can do so quickly and easily.

At the moment, we have five employees using the solution. They are data scientists and engineers.