Microsoft Azure Machine Learning Studio Reviews

The product's standout feature is a robust multi-file network with limited availability. Microsoft has been highly active recently, updating the finer details.

The regulatory requirements of the product need improvement. Many customers, including government clients, need data processing on the cloud. However, because of these regulatory requirements, I cannot use the website's machine learning and data features. I have to do everything manually, which is very time-consuming. I am trying to save the metadata on the cloud and the people's data on-premises. Microsoft should improve the configuration process. Additionally, access to accessible sources from the mobile console should be available.

I have been using Microsoft Azure Machine Learning Studio as a reseller and lead partner for three or four years.

The solution is stable.

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.

We have used Oracle before.

The product's pricing is reasonable. However, we do not have the option to limit data usage. In some accounts, we cannot control data usage and give customers enough budget for their consumption.

They should work on adding a threshold for data usage so that customers can set their limits. It would be a great way to give customers more control over their Azure Machine Learning costs.

I prefer using Microsoft Azure Machine Learning Studio, which is a powerful tool that can be used to build and deploy machine learning models. I recommend it for small and medium businesses.

I rate it a seven out of ten.

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.

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.

I use the solution to create a data flow and map all the databases or users.

The drag-and-drop interface is good.

The solution must increase the amount of data sources that can be integrated. Many customers have different types of data sources. The tool only supports seven out of ten data sources. The tool must increase the integration of data sources.

The tool is used to create flows. Its stability does not matter much as far as it creates the flow. Once we have created the flow, we just need to deploy it in our environment. Once the flow is defined, we put the algorithm in the machine learning node.

The product will be only used by a couple of people who design the flow and the model. There might be only three or four users in an organization with 100 employees.

The product is cloud-based.

The product is not that expensive.

Scalability is irrelevant to the tool. BFSI and IT companies use the product in India. Everyone is trying to leverage AI. The market is going towards AI. I see a lot of opportunity in it. The consumption of AI will increase in the future.

I will recommend the solution to my clients. We can support them because we are a partner with Microsoft. The solution enables customers to design flows using most of the available data sources. They can also create algorithms for predictive analysis. Overall, I rate the product a seven out of ten.

I use Microsoft Azure Machine Learning Studio primarily to develop small-scale machine learning models in the UI and later deploying them to the vendor for machine learning purposes.

I find Microsoft Azure Machine Learning Studio advantageous because it allows integration with Titan Scratch and offers an easy-to-use drag-and-drop menu for developing machine learning models. In my experience, I haven't identified any specific features that need improvement. I appreciate its simplicity and prefer it not to become overly complicated. For more sophisticated tasks, I would turn to other solutions like DataBricks, but for simplicity and ease of use, Azure Machine Learning Studio works well for me.

In future releases, I would like to see better integration with Power BI within Microsoft Azure Machine Learning Studio. This full integration would enhance the overall functionality and usability of the solution, creating a seamless experience for users.

I have been using Microsoft Azure Machine Learning Studio for the last six years. 

On a scale from one to ten, I would rate the stability a solid ten. From my personal perspective and experience, it has been extremely stable and reliable.

As for scalability, I would rate it a six. While it meets my current needs and expectations, there is room for improvement in terms of scalability for larger or more complex projects. However, considering that Azure Machine Learning Studio is designed as a compact and versatile tool, I don't have high expectations for extensive scalability beyond its current capabilities.

In general, Microsoft is responsive to community feedback, which is positive. However, their first-line support can be quite frustrating and is often considered a disaster. Dealing with the initial support team can be time-consuming and unproductive, as they often lack knowledge about the product or the specific issue being addressed Microsoft should implement better protocols to quickly escalate issues to higher-tier support with more expertise and knowledge about the product.

Neutral

The pricing for Microsoft products can be complex due to changes and being cloud-based, so it's not straightforward. I've been familiar with it for years, but sometimes details about product licenses and distribution can be unclear. For Microsoft Azure Machine Learning Studio specifically, I would rate the price a six out of ten.

I would recommend Microsoft Azure Machine Learning Studio, depending on the problem you're trying to solve. For our organization, we've seen benefits in marketing, particularly in calculating customer lifetime value. It's useful because it doesn't require much time to develop and provides immediate business results. I would rate it an eight 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.