Microsoft Azure Machine Learning Studio Reviews

My main use case for Microsoft Azure Machine Learning Studio is in projects requiring quick deployments, for example, when I trained an AI diabetes risk prediction model, applying the AutoML features.

What stands out for me about my use of Microsoft Azure Machine Learning Studio for AI diabetes research is its low code interface, which allows even non-expert users to experiment with models using drag-and-drop components.

The best features Microsoft Azure Machine Learning Studio offers include deep integration with Python notebooks and Azure Data Lake, which allows me to import external data, and through the pipeline, I can build my models, performing what is called data injection for my model building, making that deep integration quite interesting to use.

Microsoft Azure Machine Learning Studio is a powerful platform for those already in the Azure ecosystem because it allows for scalability and provides a good environment for reproducibility, as well as collaboration tools, all designed and packaged in one place, which makes it outstanding.

Microsoft Azure Machine Learning Studio has positively impacted my organization by reducing our project delivery times and increasing the pace at which we work, allowing us to focus on other more important tasks.

Using Microsoft Azure Machine Learning Studio has reduced our model development time from approximately four hours to about two hours.

The initial setup can be a bit challenging for someone new, as the learning curve can be steep, but once I master the platform, I find it quite manageable.

I would love to see the integration of a direct voice feature in Microsoft Azure Machine Learning Studio for easier commands and operations.

There is always room for improvement, and I expect Microsoft Azure Machine Learning Studio to continue iterating and focusing on a human-centric design approach.

I have been using Microsoft Azure Machine Learning Studio for three years now.

The deployment of Microsoft Azure Machine Learning Studio in my environment is actually easier, about 80% easier.

My experience with the configuration process was straightforward, although the initial learning phase required time to understand the documentation.

In my experience, Microsoft Azure Machine Learning Studio is stable; I have not had much challenge with it as everything works fine when programmed correctly.

Microsoft Azure Machine Learning Studio's scalability has been beneficial, as I could increase my compute resources when needing more data injection.

The customer support for Microsoft Azure Machine Learning Studio is quite responsive across different channels, making it a cool experience.

I would rate the customer support an eight out of ten.

Positive

Before switching to Microsoft Azure Machine Learning Studio, we considered AWS, but we opted for Azure first.

The initial setup of Microsoft Azure Machine Learning Studio was rigorous for someone new like me, but mastering it made things simpler.

I have seen a return on investment from using Microsoft Azure Machine Learning Studio in terms of workload reduction, as we now complete the same projects with two people instead of five.

The pricing for Microsoft Azure Machine Learning Studio is reasonable since it's pay as you go, meaning it won't cost excessively unless specific resources are used.

I did not evaluate other options before choosing Microsoft Azure Machine Learning Studio.

I looked into AWS before deciding on Microsoft Azure.

My advice for companies looking to use Microsoft Azure Machine Learning Studio is to leverage the opportunity to scale up their production and fast track their operations. I rate this solution 9 out of 10.

We just started using Microsoft Azure Machine Learning Studio for some of the business requirements, particularly for report generation. We began integrating it with our applications and are exploring its use cases with OpenAI. We are working on case management and incident management based on our business objectives.

Azure Machine Learning Studio provides a platform to integrate with large language models. It supports identifying challenges when moving proofs of concept into production. 

Currently, we are doing some POC work in report generation, which is a high-level application, even though we are in the early stages.

I cannot comment on specific improvements yet as we are still exploring and need more time to identify the areas that require enhancements. We anticipate understanding the needed improvements better once we move into a production environment.

We just started integrating it with our applications.

We are building Azure Machine Learning Studio as a scalable solution. However, we are unsure how it will handle growing data and maintain accuracy.

I am not directly involved, but we have a Microsoft support team assisting us. Some people are helping with the issues we face.

Neutral

Previously, we used core machine learning solutions and started exploring Microsoft Azure Studio for a more integrated approach.

The initial setup is somewhat straightforward, but some challenges arise, especially when dealing with data across different platforms such as AWS and on-premises systems. If data is already in Azure, the process is easier.

We are working closely with developers to explore the functionalities and build the solution effectively.

We are using AWS Bedrock in parallel with Azure Studio.

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

I use Machine Learning Studio for system reselling and integration.

Machine Learning Studio is easy to use, with a significant feature being the drag and drop interface that enhances workflow without any complaints. It provides a return on investment and cost savings, proving beneficial for company use.

In future updates, I would appreciate improvements in integration and more AI features. Additionally, easier customization and configuration would be beneficial.

I have been using Microsoft Azure Machine Learning Studio for approximately one and a half years.

I rate the stability of Machine Learning Studio more than nine on a scale from one to ten.

I rate the support from Microsoft as five out of ten. It could be improved.

Neutral

We chose Microsoft Azure Machine Learning Studio based on our customers' choice.

The installation requires less than one hour.

I handle the installation by myself without assistance from colleagues.

We are currently using Google as another vendor's product.

I would recommend Machine Learning Studio to others. On a scale of one to ten, I would rate the overall solution as a seven.

Neutral

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 use Microsoft Azure Machine Learning Studio to train our models and for data labeling.

The solution's most beneficial feature is its integration with Azure. We are an Azure-based company, and the solution's integration feature allows us to log in through Cosmos DB or Application Insights.

Lately, we have had some issues with the solution regarding labeling jobs. We can create a label job, but we still have to use the Azure Machine Learning REST APIs, which are not yet supported in the Python SDK version 2.

Microsoft has a lot of documentation, but you can do it using the CLI, UI, or Python SDK version 2. You can have 100 ways of working, while I would like to have one way of working. It's very difficult to know what is best, according to Microsoft.

I have been using Microsoft Azure Machine Learning Studio for 2 years.

I rate the solution an eight out of ten for stability.

I rate the solution a seven out of ten for scalability.

We have support using our enterprise subscription. You need time to log a ticket and confirm your problem, and somebody gets to you and asks a few questions to understand the problem very well. Bit by bit, you have to go through different stages, but in the end, most of the time, you get a solution.

Positive

Installation of the solution is difficult in industrial environments because it has to be secured and with private endpoints. It would be nice if some good Bicep templates were available so that everything could be set up in one way.

The solution’s deployment took a few days. Around two to three people were needed for the solution's installation and configuration, which has a steep learning curve.

Last year, we paid 60,000 for Microsoft Azure Machine Learning Studio in our department. The more power you need from the solution, the more expensive it is.

On a scale from one to ten, where one is cheap and ten is expensive, I rate the solution's pricing a seven out of ten.

I would recommend the solution to other users.

Overall, I rate Microsoft Azure Machine Learning Studio an eight out of ten.

We used this product as implementers. For example, a client wanted to use the Azure stack, specifically Azure Machine Learning Studio. Microsoft was a consultant on the project, and we were the implementation partner.

There are actually two tools. One is Azure Machine Learning Designer (which used to be called Azure Machine Learning Studio), and the other is Azure Machine Learning. Designer is a drag-and-drop interface, primarily for those without extensive coding expertise.

Azure Machine Learning has become the de facto product, and it allows you to write code and provides numerous components for building machine learning models.

We build all our machine-learning models using ML Studio. That's the primary purpose for us. We build standard models that use data and various algorithms.

My expertise was focused on the analytics aspect. We worked in a large firm with different teams responsible for MLOps and integrating outputs with other systems. 

My responsibility was to get data, build models, and provide the output. How the output was rendered and the process of doing so was handled by other teams. 

Additionally, there's a separate team that handles the productionalization of machine learning models. It's a diverse team structure, so the scope of the totality of the integration process varied. Essentially, they take the model I build, use Azure DevOps for versioning, and then deploy it to production.  

Everything in Azure is very intuitive. As a Microsoft product, it's designed that way. You can easily search for things, and many features are built-in. Many things can be easily done via drag-and-drop.

We often use custom code that we can template or create as boilerplate code for different teams. It sounds difficult, but Azure makes it very easy. 

The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.

The drag-and-drop interface specifically improves our workflow a lot. Designer simplifies the process when we want to create a quick model. It makes things very, very easy. It's a great starting point.

When we want to create models, model management and deployment are crucial. We had boilerplate code ready, and Azure made it significantly easier to deploy using its services. That was the easy part.

In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions. Azure didn't have that same inbuilt feature for website traffic or analytics, unlike Google DB and BigQuery.

I started using it around the year 2020. We used it up to 2023, so roughly two years.

It is a stable product. I would rate the stability a nine out of ten. 

It is a scalable solution because it works well for large databases.

I would rate the scalability a nine out of ten. 

The customer service and support are good. 

Positive

We've used Google Vertex AI and AWS SageMaker.

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. 

I would rate the pricing an eight out of ten, with ten being very expensive. Not very expensive, not very cheap.

It was on a yearly basis, and there were also usage-based costs.

If it's my recommendation, it's a very competent product. It has all the necessary features for data engineering, data science, and model management. 

It's a complete suite of products that can address your end-to-end data needs.

Overall, I would rate the solution a nine 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. 

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 use the solution in my company for some product prediction purposes and in the retail processes. My company collects data in Azure and Cynet. My company develops some ML algorithms in Microsoft Azure Machine Learning Studio. Two years ago, I had a training session with Microsoft, so now I can develop ML algorithms in the retail sector.

The high price of the product is an area of concern where improvements are required. The product needs to be available at a cheaper price. I think the tool needs to be made available to children for free, especially if you have a student's or a teacher's email ID from a university.

I have been using Microsoft Azure Machine Learning Studio for two years. I am a user of the solution.

It is a stable solution. I have seen a lot of products, but I feel that Microsoft Azure Machine Learning Studio is the most stable tool in the market. Stability-wise, I rate the solution an eight out of ten.

It is a scalable solution. Scalability-wise, I rate the solution an eight out of ten.

For development purposes, there are ten people, and since my institute expanded its use, I think it will increase to fifty.

I plan to increase the tool's usage.

I got support for the tool in Turkey with the help of Mircorsoft's admins and consultants and they were okay. I rate the technical support an eight out of ten.

Positive

I evaluated DataIQ against Microsoft Azure Machine Learning Studio. Most of the applications I used were small or niche tools, while Microsoft Azure Machine Learning is huge and expandable.

The product's initial setup phase is easy. I rate the product's initial setup phase as an eight out of ten.

The solution is deployed using Azure's cloud services.

The solution time for deployment can take a few hours. I work in an enterprise with a medium range of data.

I rate the product price as a nine on a scale of one to ten, where ten means it is very expensive.

You can take care of the input of the datasets, and then Microsoft Azure Machine Learning Studio will provide everything for you. The tool has drag-and-drop options, and it becomes easy to develop anything, acting as a time-saver.

The drag-and-drop options in Microsoft Azure Machine Learning Studio influence the machine learning workflow as it allows you to put in some links, and it integrates with nearly all of your systems. You can use the datasets with the tool program to see the features and dimensions of all the datasets. Then you can choose some ML algorithm about the datasets, and it recommends the program, after which you can test a lot of models, but normally, you can't do it because it's not in Python or something else, leaving you confused. In Microsoft Azure Machine Learning Studio, there is a system from beginning to end that covers everything one should do.

The tool does have an influence on performance as it makes things faster and more productive.

I can recommend the tool to my colleagues. It provides the developer or product owner the ability to watch the system overall. If you use the tool you can begin learning about ML and you become more effective and faster which is important for enterprise.

I rate the tool a nine out of ten.