The initial setup of this solution is straightforward.

The client site that we were working at had a proxy, and we were having a lot of trouble managing the rules inside the proxy because the Machine Learning Studio was not showing on the screen, in the browser, as it should. There are a lot of JavaScripts and this is a heavy client. There is a lot of feature logic performed on the client-side, such as the drag-and-drop. We had a lot of problems.

Besides that, once we fixed our network problem, it was straightforward.

The initial setup is straightforward with deployment time depending on the environment. It depends on how many machine learning models we need to develop, the type of resources, the different sources, data volumes, etc. 

We can deploy the solution within ten minutes. 

There is a team that handles the deployment. 

We don't have to really worry about maintenance; we're still in the process of adoption.

This is easy to deploy. I did not fid the process to be overly complex. 

The solution's initial setup process was pretty straightforward.

The initial setup depends on the developer's knowledge of machine learning models as to whether it is easy or difficult. With a good understanding of these models, then we can get to work quickly in the environment. With 20 years of experience in IT, making applications on legacy platforms and non-web platforms, I have found that Azure has a very good implementation. The platform is so comprehensive that it doesn't matter what kind of work we do, we can find the tools needed to get the job done. In comparison to what I was doing five years ago, Azure is a great platform and I really enjoy working with it.

We should allocate up to 12 percent of our project time to deployment. Depending on the complexity of the solution, we should expect to spend more time on deployment.

I rate the initial setup phase of the solution a six on a scale of one to ten, where one is difficult, and ten is easy. The initial setup phase of the solution was a bit complex. The setup phase is a bit difficult if you want to view Microsoft Azure Machine Learning Studio as an application.

The solution is deployed 50 percent on the cloud and 50 percent on-premises.

Considering the fact that my company currently builds some standard solutions, Microsoft Azure Machine Learning Studio's deployment takes us around two to three months.

In terms of setting up Microsoft Azure Machine Learning Studio, initially, when my company started, the documentation wasn't so good, but now it has improved. Provisioning the solution only takes a few clicks, so it's no big deal, but setting up the pipelines because no enterprise will have a single environment, you'll have to create multiple pre-production and end production environments, so moving my latest changes to the next environment was a bit of a challenge.

Many terminologies are now in the market such as DevSecOps, and MLOps, so that MLOps documentation was available initially, but it wasn't very explanatory, but now, there's a lot of improvement in the MLOps documentation and that will help me move and propagate my changes from one environment to another.

Microsoft has made improvements into the tutorials, especially on MLOps. Finding MLOps experts in the market was also very tough initially, so my company was trying to learn on the job and do it, so it took some thinking and time, but it's still good because you can learn on the job and do it, but you won't always have the luxury of time to learn it.

I found the setup to be very easy. I've been doing this type of work for 50 years so the modern terminology isn't always the same as what I grew up with. It took me a while to understand that, but the setups were very easy. As with anything, the hardest part is always getting the data together, but the outside consultants had built up a very, very good data warehouse. The ability to manipulate the data and create variables was very nice.

THIS IS THE ONLY MODELING APPROACH THAT EVER WORKED THE VERY TIME I RAN IT!!

It was very simple and straightforward. It is really simple to start building a project.

I rate the solution a seven out of ten for the ease of its initial setup.

The initial setup for me was initially quite complex, but after completing a course related to Microsoft Azure Machine Learning Studio, it became less complex. However, one needs to have a good understanding of the required parameters and what the model needs to do in order to achieve good performance. So sometimes, it's not that simple. The deployment process took me a couple of hours to complete. I was able to do it quickly because I was using Azure Machine Learning Designer and Python SDK while also learning automation. The setup process for AltaML was easy and could be completed in hours. With Python SDK, the setup process was quite long because of the code that needed to be written, so one needs to know what to write.

We have three people that can handle deployments. It takes about two months to deploy. 

We provide maintenance to our clients and only need one person to handle it. It's not too maintenance-intensive. 

The deployment is quite easy. It takes a few minutes. I rate the ease of deployment a seven out of ten.

The initial setup wasn't too complex, and I would rate it at eight out of ten. The documentation was easy to follow.

The deployment took a couple of days. We obtained the data, made it available, and then set up the environment. We tried out different models and ran experiments.

The initial setup of Microsoft Azure Machine Learning Studio is easy.

Setting up Microsoft Azure Machine Learning Studio was very easy and is comparable to how easy it is to use any feature available in the tool.

Configuring the pipeline takes just ten to fifteen minutes, but that would still depend on the data volume.

The solution's initial setup process is complicated. We need to get details on web service activities, identify internet services, manage service identity, etc. The time taken for deployment depends on the complexity of the specific model. It takes around a quarter of an hour per model to complete, on average.

We didn't have any problems with the setup. It was pretty straightforward.

The initial setup was fairly straightforward. I would rate it seven out of ten.

One or two engineers can easily maintain ML Studio without much hassle.

Deployment of the tool is simple. Just one click on Microsoft. Once you have procured the license, you can just log in and use it. It's a ready-to-use tool.

When you deploy the solution after analytic development, it depends on the project but it can take anywhere from one month to six months.

Also, depending on the infrastructure, the initial deployment can take one week to a month.

The initial setup is quick and easy. It's not complex at all. There is no installation per se. It's simply that you plug into the cloud and start using it.

For deployment, you likely need a two or three-member team. You don't need a lot of people to get it up and running. Largely they are just managers, admins or engineers, or a combination of those three.

The initial setup is straightforward.

Our deployment took about six weeks, but that was also integrating the new telephony platform as well. For the AI elements, it was probably around five days.

Once the initial knowledge base was set it it took time to build and get it to where we needed it to be. Until that happens you can't really implement the AI element. This is what took about six weeks, so that it covered all of the inquiries that we wanted.

We started with an on-premises deployment and have moved to the cloud.

Whatever we develop, we deploy from the GUI. The tool can be easily deployed.

The initial setup is easy. I rate the process an eight out of ten. We have trained machine learning models for the installation. It requires two executives for deployment and three executives for maintenance.

The installation is easy. However, there is a bit more to do than with the installation of Databricks. The time it takes for the installation is approximately one day with a two-person team.

Compared to their big competitor, it's much easier to set up.

The initial setup is very straightforward. It is not difficult to do.

The infrastructure and the software configuration part was done by one of my teammates. It was completed in two working days. We did experience some issues with the board communications which extended the time to complete the setup. This was only for the DataStage installation which is one of many components of this solution.

Simple. Create MLS live account (preferably paid ones), open MLS, done.

Caveat: Different organizations have different attitudes towards cloud use, especially with sensitive data. At Bridgestone, the hardest part was getting corporate approval to allow me to upload heavily treated, sensitive data to a cloud platform.

I was not involved in the deployment process. But there is maintenance. It was quite a headache. 

Maintenance does require attention. With any cloud implementation, cost optimization is a major factor. Our team had discussions about it. 

The initial setup was straightforward, though you do need some experience with Azure administration in order to install it.

The initial setup was not difficult or overly complex. It's very straightforward, very simple, and very easy to understand. 

Everything is just written down in a way that was an easy way to understand, even for someone who isn't used to the packages of Microsoft.

The initial setup is very straightforward.

 Compared to similar solutions, Microsoft Azure Machine Learning Studio is quite new so the initial setup wasn't much of a challenge. The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team.

Setting up ML Studio is very straightforward because it's a cloud thing. 

The initial setup is straightforward. I think it's a cloud service, and whenever I create a new workspace, it takes me around five to ten minutes to deploy it.

The initial setup is straightforward and not too complex.

It was complex to setup the workspace, but once it was done, we were good to go.

The initial setup was very easy because it's a cloud solution. With the cloud option, you just subscribe, and you are ready to go in a few minutes.