Microsoft Azure Machine Learning Studio Reviews

My company uses Microsoft Azure Machine Learning Studio to help our company's customers view AI solutions.

My company's clients' use cases will be that they use the solution to feed information to the system about their customers who purchase from them. The solution also helps one to combine products to engage in cross-selling and upselling activities while keeping track of customer lifetime value. The solution also helps its users with the pricing simulation part to figure out what prices are good for the business and maximize the closing of the sale.

The most valuable feature of the solution is the availability of ChatGPT in the solution.

Improvement will be possible with more machine learning functionalities in Microsoft Azure Machine Learning Studio since, at times, the current accuracy of the solution is not good enough. It would be good if Microsoft Azure Machine Learning Studio could have a generative AI tool similar to ChatGPT.

I have been using Microsoft Azure Machine Learning Studio for three years. My company functions as a reseller and a partner of Microsoft.

Stability-wise, I rate the solution a seven out of ten. With ML, you may face some data-related issues, especially considering that when dealing with customers at times, the data that comes in might not be clean. Stability-wise, you may face certain problems when you fail to refresh the data in the solution.

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.

I rate the technical support a six out of ten.

Neutral

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.

I rate the solution's pricing a four on a scale of one to ten, where one is cheap, and ten is expensive.

There are some additional payments to be made apart from the licensing fees of the solution since buying Microsoft Azure Machine Learning Studio alone won't make it a complete solution. You will need the database and data lake services.

Microsoft Azure Machine Learning Studio does not allow users to have a PnP option, like an ERP or a CRM system, where everything works if you include the data with the system. Sometimes, it is difficult to generate good patterns using the solution. You need to have good experience with the solution to move around with the data from the beginning before coming up with different strategies to end different problems. In general, the product is not a straightforward solution.

There is a need for Microsoft Azure Machine Learning Studio's users to put in some programming efforts to make the solution work accurately under different scenarios.

I rate the overall solution a six out of ten.

I've had experience working for two distinct companies. My previous employer operated in the telecom domain, primarily focused on telecom-related projects. In my current role, which is in the shipping domain, we primarily manage shipping cases. Furthermore, our current work predominantly revolves around a machine learning platform implemented on storage systems.

In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally.

One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform.

I have been working with Microsoft Azure Machine Learning Studio for three years. 

I would rate it eight out of ten. 

I would rate it eight out of ten.

Yes, I have worked with Azure in my previous experiences.

There isn’t any such expensive costs and only a standard license is required. 

We primarily use the solution for our projects. We're currently adopting it for a competitive, new initiative. We create a lot of products related to AI inside the organization as needed for business cases and different business deals. We use it for data extraction and language processing. 

They've been helpful with hands-on experience.

It's easy to use.

The deployment is fast.

The interface has been very good so far. 

It has good configurations. 

It's stable.

The solution scales well.

There have been issues with environmental creation. It can take a lot of time. The speed of deployment should be faster, as should testing. 

We've been using the solution for about six months. It is fairly new. 

It is a stable solution. It's reliable. I'd rate its stability ten out of ten. There are no bugs or glitches, and it doesn't crash or freeze. 

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. 

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.

Our team handles the deployment in-house. 

We are customers and end users.

We're using the latest version f the solution. 

I'd rate the solution ten out of ten. 

The use cases of this product are primarily for the BFSI; digitization and building machine learning models that provide recommendations for creating analytical insights from extracted data. We also do Jupyter Notebook authoring. We are partners with Microsoft and I'm a practice director.  

The product enables quick data preparation and data processing pipeline as well as modeling work and it's all part of Azure Machine Learning. It also gives us an idea of what machine learning model is good to use because the hyperparameter tuning is done automatically which saves us time and effort. 

Auto email and the studio are great features. 

It's not that easy to master the program, it requires some specific learning. If we want to extend the program to include inexperienced users, it can take some time for them to learn the solution. It would be nice if they added GPU solutions. Most of the solutions coming out now are video analytics or edge computing-based and Azure should have that focus.  

We haven't had any issues with stability. 

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. 

Customer support is very good, they are prompt and helpful in solving problems. 

Positive

Our switch to AMLS was an organic development that came from the needs of our customers and was based on the quick time to develop and the pre-built machine learning models that the solution has.

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 don't deal with licensing, that is something our customers are responsible for.  My understanding is that the cost is $50 for the digitization of 1,000 pages. I think it should be reduced to somewhere between $20 to $30 per 1,000 pages so that we can make a better offer to our customers. 

I believe Azure Machine Learning has a very good pre-built model which enables quick development of solutions, particularly text analytics and cognitive-based solutions. 

I rate this solution nine out of 10. 

I use Microsoft Azure Machine Learning Studio as a component in more complex stories involving Azure AI and Azure integration with business needs.

I can smoothly integrate with other Azure services, which is a valuable feature. The platform as a service provides user-friendly instruments, making the experience easy. 

Additionally, the integration with Azure services enhances workflow and meets my expectations.

The pricing policy should be improved. I find the pricing to be not a good story in this case, as it is not affordable for everyone.

I have been using Microsoft Azure Machine Learning Studio for about two years.

Stability is high, with a rating of nine out of ten.

Microsoft Azure Machine Learning Studio is scalable.

Microsoft technical support is rated a seven out of ten. There used to be better times for Microsoft technical support.

Neutral

The initial setup involves provisioning and configuration rather than installation. It is a short story, taking about ten minutes per service for proper configuration.

My team of specialists handles the implementation. We are a system integrator with a team of around two hundred engineers, twenty-five of whom work in AI and data.

Measuring ROI is hard due to the nature of projects, however, Microsoft provides a business case builder to help estimate potential profits and values from its products.

Pricing is considered to be top-segment and should be improved. I rate the pricing as three or four on a scale of one to ten in terms of affordability.

I would recommend trying and using it as it will be okay. I rate the overall solution eight out of ten.

We have data from our business, and we want to make AI models. The question is how we want to use those models in our business. That's what we're going to do next year.

The product is well organized. The thing is how we will get the models to work within our code. We have some suggestions there, but we want to gain more experience and be ready to answer that because we are currently working on this and don't have all the answers yet.

The tool is well organized. What I am very happy about is the ease of deploying new resources. You can easily create your pipeline within minutes.

One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. 

The tool should keep on updating new algorithms and not stay static. 

I have been working with the product for ten years. 

I rate Microsoft Azure Machine Learning Studio's stability as nine out of ten. 

I rate the solution's scalability a ten out of ten. I am the single user of Microsoft Azure Machine Learning Studio. 

We haven't had any experience with the tool's support because we didn't use it. We are mature developers and don't need it at this time. We don't have any complex business needs.

The tool's deployment time depends on the resource you will deploy. Some resources are deployed within minutes, while others may take more than 15-20 minutes. I have deployed mostly web applications, REST APIs, and databases.

We're trying to provide robust solutions to our customers, which previously involved multiple steps. Now, we're going to provide it in one step. That is our benefit because the customer will get a final solution, not a solution in steps. We will formalize and streamline them to align with our new solutions.

We pay only the Azure costs for what we use, which involves some subscription costs. But essentially, you pay for what you use. There are no extra costs in addition to the standard licensing fees.

We are trying to find some commercial value. I have learned how to use it, and we will integrate it into the project. That's our next goal.

I rate Microsoft Azure Machine Learning Studio a ten out of ten. If you want to use it, get the certifications, and then work on some projects to gain more experience.

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.

Our use cases  involve customer segmentation for targeted marketing, where I use machine learning to identify potential customers interested in a new product. Another is a recommendation system on our company website, where I use machine learning to suggest additional products to customers based on their browsing or purchase history. Lastly, there is pricing estimation, where I use machine learning to predict the price of an item or article.

The features of Azure Machine Learning Studio that I find most valuable depend on the type of model I'm working with. For integration, knowing halfway indicators is crucial to assess model performance. For classification models, the confusion matrix is important for evaluation, while for regression models, statistical tests like the provision statistics are valuable.

One area where Azure Machine Learning Studio could improve is its user interface structure. Simplifying the initial information presented upon first use could make it more accessible, especially for users with limited technical skills. Providing only essential information upfront would enhance the user experience and reduce complexity.

I have been working with Microsoft Azure Machine Learning Studio for three years.

I would rate the stability of the solution at a six out of ten. Improving stability involves finding people with the right skills to handle problems that arise. While stability depends on how well the solution is installed, ongoing efforts are needed to address issues and refine the system. We are working step by step to identify and solve problems, but there's room to find more comprehensive solutions as they come up.

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.

Before Microsoft Azure Machine Learning Studio, we used on-premises solutions. We made the switch to Azure Machine Learning and the cloud to modernize our projects and leverage the benefits of cloud computing.

I use Azure Machine Learning Studio for predictive modeling in my project. I follow a workflow that involves selecting data, preprocessing it, training models, and deploying them. The Studio's tools cover all these steps, making it convenient for me to build and deploy predictive models.

In a specific scenario, I used Azure Machine Learning Studio for data preprocessing by creating new variables. This involved tasks like transforming variable types or combining multiple variables to create new ones. Additionally, I employed cross-validation techniques, such as k-fold validation, to assess model performance and select appropriate metrics for evaluation.

The most important aspect of my machine learning projects is the quality of the data. It is crucial to determine whether the data can provide meaningful information relevant to the project's use case, regardless of the specific tools or features used.

The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow. It is easy to use and increases productivity by allowing quick experimentation and visualization of data pipelines. This feature enables me to iterate rapidly and efficiently, especially for small projects or presentations.

I would rate the performance of the solution at an eight out of ten for my team. However, our data volume is not the largest. While I believe our performance is strong, other companies might rate it lower due to different circumstances.

My advice for someone considering installing Azure Machine Learning Studio is that it is user-friendly, especially for technical users. You can easily upload data and analyze it with the examples provided. The drag-and-drop interface makes it intuitive, and upgrading to this tool for data analysis is a good idea.

Overall, I would rate Azure Machine Learning Studio as a nine out of ten.