Microsoft Azure Machine Learning Studio Reviews

Microsoft Azure Machine Learning Studio can be used for developing models, such as predicting energy usage, as I did for my bachelor's project, where I predicted future energy usage for a city in Norway. The solution can also be used for classification tasks, such as identifying objects in images.

In terms of features, I personally find Azure to be clearer and better than Google because it provides better quality and clarity regarding what needs to be done.

The icons in the solution could be improved to include examples of how to use each container, as sometimes it's unclear which container to choose. It would be helpful to provide examples to understand better which virtual machine or how many courses to use. Overall, the icons in the solution could be improved to provide better guidance to users.

Additionally, the setup process for the solution could be made easier.

I have been using Microsoft Azure Machine Learning Studio for half a year. I am a student and user of the solution.

I think Microsoft Azure Machine Learning Studio is more stable than Google.

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.

Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful. It is often the case that everything one needs is already in the documentation, so I haven't had to use the support much. Even when I have reached out for support, I have always received a prompt response.

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.

I used the free student license for a few months to operate the solution, but I'll have to pay for it if I want to do more now.

Before choosing Microsoft Azure Machine Learning Studio, I only evaluated Google Cloudpath.

If you plan to use this solution, I suggest you not be intimidated by its complexity at first. You will gain more clarity regarding the solution over time with perseverance and practice. Overall, I rate the solution an eight out of ten.

In terms of use case, we implement Microsoft Azure Machine Learning Studio using Python libraries, so basically, we have a centralized studio where we just have to drag and drop features and create the model out of the data that we have. Microsoft Azure Machine Learning Studio is pretty easy to use even for people who don't know much about coding. They just need to know the features and libraries, so it's similar to Tableau and Alteryx because users can drag and drop features to create models or pipelines. We create and deploy pipelines through Microsoft Azure Machine Learning Studio.

What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use.

Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it.

Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it.

What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners.

I've used Microsoft Azure Machine Learning Studio in the past year in my previous company, though I'm unsure about which version I was using at the time.

The functionality of Microsoft Azure Machine Learning Studio, specifically its underlying computing power, was managed by Azure, so stability-wise, it's a good solution.

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 company decided to go with Microsoft Azure Machine Learning Studio because of the partnership with Azure Cloud, so it's a way to leverage all features. The data was also hosted on the Azure platform, which made it pretty straightforward to use Microsoft Azure Machine Learning Studio rather than integrate with other tools.

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.

My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it.

Approximately two hundred to three hundred people, mostly part of the data analytics team, were using Microsoft Azure Machine Learning Studio within the company.

My advice to anyone using Microsoft Azure Machine Learning Studio for the first time is to have an understanding of machine learning, deep learning, and libraries. You should also know the scripts because features are created on top of the machine learning libraries used in Python. If you want more optimizations or a better accuracy rate, you need a proper understanding of machine learning or a machine learning background before using Microsoft Azure Machine Learning Studio.

I'm rating Microsoft Azure Machine Learning Studio eight out of ten because it still needs some improvement. For example, because the drag-and-drop feature of the tool was written or based on Python, when you're creating a model in Microsoft Azure Machine Learning Studio, you'll get good accuracy by writing the script in Python, so accuracy isn't standard. You can customize your metrics to get good accuracy, but what you'll get is completely generalized, so whatever use case you feed into the pipeline, it'll create a test to get good accuracy, but it'll not give you a guarantee that this will be the only accuracy you'll get.

The previous company I worked in was a partner of Microsoft Azure Machine Learning Studio.

We're mainly using Microsoft Azure Machine Learning Studio to run experiments on our data for predictive analytics.

I like being able to compare results across different training runs. The hyperparameter tuning function is a valuable feature because it provides the ability to run multiple experiments at the same time and compare results.

The initial setup time of the containers to run the experiment is a bit long.

I've been using this solution for about a year.

It's pretty stable, and I have not had any issues. I would rate the solution's stability at nine 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.

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.

We deployed it ourselves.

On a scale from one to ten, with ten being overpriced, I would rate the price of this solution at six.

If you want to train models on larger datasets, then Microsoft Azure Machine Learning Studio is a good solution. If you need to run a few diverse set of experiments with different environments, then it really comes in handy.

Overall, I would rate Microsoft Azure Machine Learning Studio at eight out of ten because it's easy to start using it. Also, it's pretty robust and stable, and the interface is nice to work with.

Microsoft Azure Machine Learning Studio can be used for a variety of machine learning tasks, including deployment and creation of new components.

The stability and performance of the solution are good. But there is nothing specific to point out since it works smoothly.

Though I won't outrightly state it is an expensive solution, I think it should be made cheaper for certain people.

I have been using Microsoft Azure Machine Learning Studio for six to eight months. There are no versions of the solution since it is a complete set of tools that Microsoft provides. Hence, I highly doubt if there is some version.

It is a pretty stable solution. Stability-wise, I rate the solution a nine out of ten.

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 have never contacted the technical support team of Microsoft since I never need their help.

The solution's initial setup process was pretty straightforward.

I just worked with the company, and so the installment and everything else were taken care of by their infra team.

Since I am a normal employee working in my company, I don't know whether the company has experienced any return on investment using the solution.

The solution operates on a pay-per-use model.

I can recommend the solution to others planning to use it. It is important to note that the solution is a bit costly. But, then the cost depends on the requirements of the person planning to buy it.

It's difficult to say whether Microsoft Azure is costly or not since it depends on individual needs. Time is important for some, and the tool is very time-efficient, making it seem less costly. It may appear costlier for those who don't consider time important.

Overall, I rate the solution a nine out of ten.

We use Microsoft Azure Machine Learning Studio when we need to connect with the customer's data. We can connect easily, and fast, and test and train quickly. We have quick results.

The most valuable feature of Microsoft Azure Machine Learning Studio is the ease of use for starting projects. It's simple to connect and view the results. Additionally, the solution works well with other Microsoft solutions, such as Power Automate or SQL Server. It is easy to use and to connect for analytics.

Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me.

I have used Microsoft Azure Machine Learning Studio within the last 12 months.

The stability of Microsoft Azure Machine Learning Studio could improve. The solution is good for test development but it is not good for production environments.

Microsoft Azure Machine Learning Studio

I have used other solutions, such as Anaconda previously, and I prefer them over Microsoft Azure Machine Learning Studio. They are more stable.

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

We have one data scientist for the deployment and a data analyst for maintenance of the Microsoft Azure Machine Learning Studio.

I would recommend this solution for MPPs for fast production or deployments, but do not recommend the solution for production.

I rate Microsoft Azure Machine Learning Studio a five out of ten.

The use cases actually depend on the client's requirements.

We have been working with multiple clients so they have their own use cases, they have their own problem areas, and based on their use cases, we use that platform.

One of the use cases is dealing with dealer churn.

It's easy to deploy. 

It has many features which help the person avoid delving into more technical things. It's more user-friendly from a user point of view.

The solution is stable.

Technical support is helpful.

It's highly scalable. Since it is on the cloud, you can expand the storage, you can expand the RAM, and all those things. The best thing is the scalability.

Technical support could improve their turnaround time. 

I've been using the solution for approximately a year now.

It's quite stable. There are no bugs or glitches. It doesn't crash or freeze. It's reliable and the performance is good. 

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. 

Microsoft technical support has been wonderful. They are helpful and supportive. That said, the turnaround time can be improved a bit. 

Positive

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. 

I'm not aware of how much the solution costs. I don't handle any of the licensing. 

We're a customer and an end-user. 

We're using the latest version of the solution. 

I'd rate the solution an eight out of ten.

I use a combination of Microsoft Azure Machine Learning Studio and Azure Databricks. I mostly use Azure Databricks for building a machine learning system. There are several workflows for a machine learning tuning system that involves data pre-processing, quick modeling pipelines that execute within a couple of seconds, and complex model pipelines, such as hyperparameters. Additionally, there is a setting to set different AutoML parameters. 

For the training and evaluation phase of the whole machine learning system, I use MLflow, for a testing system and a model serving system, which is one core component of Databricks. I use it for Model Register and it allows me to do many things, such as registering model info, logs, and evaluation metrics.

The newer version of this solution has better integration with automated ML processes and different APIs. I feel like it is quite powerful in terms of general machine learning features, such as training data handily by having different sampling methods and has more useful modeling parameter settings. People who are not data scientists or data analysts, can quickly use the platform and build models to leverage the data to do some predictive models.

Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon. It has the most sophisticated set of categories of parameters. The data encodings and options are good and it has the most detailed settings for specifics models.

I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system.

The developers for this solution have not been as active in improving it as other solutions have had more improvements, such as Databricks.

Sometimes there might be some data drifting problems and this is what I am currently working on. For example, when our new data has a drift from the previous old data. I need to first work out a solution. Azure in Databricks or in Azure Machine Learning Studio both works fine. However, the normal data drifting solution is not working that well for the problem that I am facing. I am able to receive the distribution change and numerical metrics changes, but it will not inform me how to fix them.

I have been using this solution for approximately three months.

I use Databricks alongside this solution.

I rate Microsoft Azure Machine Learning Studio a seven out of ten.

Developing and operationally implementing a powerful lead scoring model for a major Multiufamily developer and operator of apartment properties throughout major western states. The work included 3 years of data across over 60 properties with more than 500,000 leads and 3 million transactions.

Increased sales force productivity by permitting them to prioritize activity during peak leasing periods on those leads most likely to close

The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.

We were working across a number of internal departments as well as some outside departments and this solution made it extremely easy to communicate across functional area because it was all in flow chart and data form so that if somebody had an issue, like changing the data set or something like that, they could point right to it and we could get that handled and incorporated into the model. It's extremely efficient on the computer. We had to do a number of resets on the data in the model and to be able to turn things around and validate the model and the new set in two hours, was just incredible for me.

It was very robust. The ability to move the objects around so easily and then communicate is really its power. Then to be able to show it to the sales and senior management, in terms of what was employed and made it very easy to get my job done.

In terms of improvement, I'd like to have more ability to understand the detailed impact of the variables on the model and their interactions. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" Azure (at least my understanding of it) doesn't provide readily accessible tools to assess from a management perspective the impact of their changing a sinimized, the better.gle value - for instance in closing a lead, decreasing response time by 10%.

I recognize that the multivariate algorithms used from decision trees to neural nets do not readily provide the coefficients for each variable ala the older regression modeling approaches. My experience over my 50 years of developing and implementing predictive models has been that more than half the value of modeling lies in improving management's understanding of the process being modeled, often leading to major organization and operational structure changes. More ability to understand the variables impacting the end result being optimized would be very useful. 

I have worked extensively with this solution for the last three years. 

I haven't had any problems with stability. 

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

I never had to use technical support.

I have used SPSS modeler (part of WATSON really) but because client was a Microsoft shop, I switched to Azure.

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!!

Because client isa Microsoft shop, everything was Microsoft in terms of having solutions like Power BI and stuff like that. Azure is very useful and very inexpensive.

The major advice I give is that clients must get the user,somebody who understands the business issues, to be deeply involved with it and the data transformation. Most people don't. And that's true for data science applications. We don't just follow the data in a big pile and remodel, we advance the process that we're modeling. Consider what transformations of the data you need to make it workable and usable.

Remember, over half the initial value of modeling is the strategic understanding provided re the importance of different variables to the model and hence the organizaion's performance. Very often the modeling identifies opportunities for changing structures, decision rules, etc. even prior to the model's actual implementation technically.

I would rate it a nine out of ten.