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.

Microsoft Azure Studio allows you to connect to multiple databases and do analysis.

The notebook feature allows you to write inquiries and create dashboards. These dashboards can integrate with multiple databases, such as Excel, HANA, or SQL Server. Connecting to various databases lets you link multiple dashboards or perform data analytics simultaneously. Additionally, the notebook feature supports version control, enabling you to commit code into a repository.

Performance is very poor.

I have been using Microsoft Azure Machine Learning Studio for the past year.

I worked with PowerBI.

The initial setup is straightforward. It is a .exe file that can be installed on your system. It is easily downloadable and open source solution. We can now easily download it from the Microsoft site and use it.

If performance is improved, it can provide a good return on investment because people often make mistakes when they are not familiar with their dataset. Microsoft Azure Machine Learning Studio can pull information from the database and summarize it effectively.

If you want to take design lessons, Azure Machine Learning Studio is the best tool.

The product can simplify some AI-driven projects because it currently has extensive database connectivity. For example, it can easily connect to various databases. However, the support for some other databases is presently limited and can be improved.

It pulls information from the database. Its good analytics capability makes integrations very simple.

Overall, I rate the solution an eight 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.

A project was handed to us before we came to this new client, which involved running a machine learning experiment within ML Studio. The good thing about the solution is the entire workflow can be easily managed in ML Studio because you can track and tag datasets, different pipelines, and multiple transformations. You can add custom code to any of the transformation bits, so it's very flexible in how you design your experiments. You can either design a pipeline or run notebooks. You can do many things, and it's very flexible for many use cases.

ML Studio is very easy to maintain. It's also very portable because it has ARM templates to export to replicate your experiments in separate environments. That's useful if you move an experiment to a different resource group because you want to run a new experiment. It has a strong role-based access control that helps you keep track of who's accessing what, and it has a very good data lineage tool that allows you to version and understand each of the experiments and their results. You have a very good track of everything, and you can easily distinguish between experiments and execution times and which parts where the pipelines are failing. ML Studio gives you a lot of identifiability for each one of the components of your entire experiment.

While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy, and when your datasets need to be distributed or parallel processed. While it offers you the capability of running distributed computing, it relies on the user to configure it. It does not do it automatically as Databricks would. It is up to the user to maximize ML Studio's use. Still, suppose you do not preemptively configure it to run everything in distributed compute or parallel jobs. In that case, it will just provision a single compute cluster and take longer than other solutions that do that automatically. ML Studio relies on user configuration to run parallel or distributed jobs. When you are new and trying to experiment with it, it could make your workflows much more costly and longer than they should be.

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

ML Studio's pricing becomes a numbers game. When you're trying to run isolated experiments with simple datasets that are easily tracked, ML Studio does a very good job with its on-demand pricing. At the same time, provisioning the solution and some other internal tools might not be cost-optimized. It might just be directly provisioned from infrastructure direct cost. As your data scales and grows and your transformations become more complex, your cost will probably skyrocket because it will do nothing natively to help you save on that end. Other platforms help you run jobs and allow you to run them distributed with a simple configuration from the UI rather than having the optimized code to do so.

Microsoft Azure Machine Learning Studio is very robust for tracking simple experiments. But it falls short when you run when you want to build an entire machine learning framework on top of it. I rate it a seven out of ten.