Microsoft Azure Machine Learning Studio Reviews

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.

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.

We use Microsoft Azure Machine Learning Studio to generate predictive sales analytics and determine customer behavior.

Through the solution's customer data analysis, we conduct customer data experiments, test hypotheses, and develop sales strategies.

The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant. The solution's data pipelines are easier to configure, and the solution provides a range of tools and libraries we can access.

It would be great if the solution integrated Microsoft Copilot, its AI helper.

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

The solution's stability depends on the fragility of libraries and the availability of services. Sometimes, the demand is very high in the public cloud, and performance and availability issues have occurred.

I rate the solution a six out of ten for stability.

Microsoft Azure Machine Learning Studio is a very scalable solution. Three people are using the solution in our organization.

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

I rate the solution a seven out of ten for the ease of its initial setup.

The solution’s deployment takes one hour.

There is a lack of certainty with the solution's pricing. The risk is the pricing is high without you necessarily knowing. The workload drives the solution's pricing. If you give it a lot to do, it will cost a lot of money. It's about committing to how much you want to pay for. You don't necessarily know what you'll get for the price level that you agree.

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.

Before choosing the solution, we evaluated Databricks. We chose Microsoft Azure Machine Learning Studio to get as close to the Microsoft pattern as possible. We have a Microsoft first policy, and therefore, unless there's a reason not to use Microsoft, we choose Microsoft.

I would recommend Microsoft Azure Machine Learning Studio to other users. I would also ask users to compare the solution with Microsoft Fabric, which is a collection of components to put a workflow together end to end.

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

Our customers use the solution for its automated machine-learning features.

The solution's learning models developed using Python coding are not robust. The AI features need to summarize vast amounts of data into simple models. It must understand all the mathematical parameters and formulas within the models for reliable predictions. They need to work on this particular area. Also, they should provide integration with Microsoft Teams as well.

We have been using the solution for three and a half years.

The solution is stable. I rate its stability an eight compared to Mathematica.

The solution is scalable.

The solution's technical support is excellent. They respond and resolve queries promptly, irrespective of the type of subscription one has purchased.

Positive

In comparison, Mathematica is more expensive than the solution.

The solution's initial setup process is complicated. We need to get details on web service activities, identify internet services, manage service identity, etc. The time taken for deployment depends on the complexity of the specific model. It takes around a quarter of an hour per model to complete, on average.

We have to pay for the solution's machine and storage. The cost depends on the specific models. Some of them cost 18 to 25 cents per hour. At the same time, some CPU machines cost €30 per hour.

The solution is easy to use. I advise others to train to know how it works while learning the mathematics behind it. I rate it an eight out of ten.

I've developed a couple of chatbots using Azure OpenAI, leveraging its documented solutions and APIs. The tools available make it straightforward to implement machine learning solutions. However, there are challenges, such as hallucinations and security issues, but overall, it works well and is quite fast, allowing for the development of interesting projects.

The main issue is identifying a solid business case. There are many exciting use cases, and we have done numerous proofs of concept, prototyping, and piloting, which generated a lot of excitement. However, determining which business case to implement, especially when it competes against other applications, becomes challenging.

Improvement in integration is crucial, and it'll be interesting to see how it develops, especially with SAP's move towards cloud-based solutions like SAP Rise and its collaboration with hyper scalers like AWS. Integrating SAP with hyperscaler machine learning solutions could simplify operations, although SAP's environment is complex. SAP has initiated deals with AWS for this purpose, but I'm not as familiar with Microsoft Azure Machine Learning Studio's involvement.

We started exploring Azure Machine Learning Studio about three years ago. We conducted POCs with it, but very few projects made it to production. After that, our company shifted to AWS. We did several POCs there, too, but none went into production. So, my experience with Azure Machine Learning Studio and AWS is mostly on the POC and experimentation side, without actually deploying any solutions into production.

The technical support is very good. We receive regular calls and have a key account assigned to our company because we are a large client. This makes it easy to get the information and help we need. However, for smaller companies that do not have a key account executive assigned, it might be a bit more difficult. Overall, the experience with the tool's technical support has been very positive.

Neutral

Microsoft takes an application-based approach with Azure Machine Learning Studio. It started as an application development company and moved into the cloud. On the other hand, AWS is built up from bits and bytes, which is a different approach. AWS offers many ways to accomplish the same tasks, which can be initially confusing. They are working to make it more application-oriented. Microsoft focuses more on solving business problems by first building application solutions, with technology supporting those solutions. 

Working with clients who prefer AWS for their hyperscaling needs, such as hosting SAP systems on the AWS cloud, aligns better with AWS products than using another hyperscaler like Microsoft Azure Machine Learning Studio. That's the advantage of choosing AWS—it offers high hyperscale capabilities.

AWS is recommended for companies that have strategically decided to prioritize security and are considering cloud providers like AWS. Initially, the main concern was security. Once security concerns are addressed, the next challenge is how well the various services integrate and work together. AWS can be a suitable choice if a company has determined that it needs flexibility and a wide range of services. Developing solutions with AWS took significant time for the company I work with.

I would rate the product a nine out of ten. Compared to AWS SageMaker Studio, it is easier to use, especially when handling data and working with Python. AWS is a bit tougher because it relies heavily on containerization, which can be tricky for organizations due to security or cost issues.

I don't know much about MLOps, especially the full circle, which includes monitoring and observability. From an experimentation point of view, the tool and AWS are good, but I'd rate Azure slightly higher because it is simpler. You don't need to understand various underlying services as much as you do with AWS. This difference is due to Microsoft's top-down design approach, coming from their application background.

Our organization employs people with diverse professional backgrounds. We have sociology, mathematics, and statistics backgrounds. We employ these people within our data science team. They require a certain amount of programming skills.

The good thing about Azure Machine Learning is they have a drag and drop feature. You can use Azure Machine Learning designer for all of your data science teams.

Any non-programmer can adopt it. All he needs is statistics and data analysis skills.                                                                                             

I used Azure Machine Learning in a free trial and I had a complete preview of the service. A problem that I encountered was that I had a model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer. I didn't find any option to upload my model, so that I can create my own block and use it in Azure Machine Learning designer.

I believe this is a problem because sometimes you have your model created on some other device and you just have a file that you think can be uploaded to Azure Machine Learning and can be tested through a simple drag and drop tool.

We have been using Azure for three months. We have been exploring it for different use cases. 

I haven't used it long enough to have found any bugs in our current system. If there were bugs I would definitely report it on their website.

We didn't have any problems with the setup. It was pretty straightforward.

It's an easy tool. They have a good level of resources and we are pretty low with resources as far as data science is concerned.

Azure Machine Learning offers an opportunity for those who haven't been introduced to Azure programming. You can use the data analytics and their statistics skills to build and deploy data science solutions that can be beneficial for society and for different organizations.

I would rate it a seven out of ten.

We use the solution to develop prompt flows.

The solution facilitates our production. Instead of running a lot of hard code, I just put my prompt flow in Machine Learning Studio, which takes care of the job.

The product must improve its documentation.

I have been using the solution for six months.

I rate the tool’s stability a ten out of ten.

Five people use the product in our organization. I rate the tool’s scalability a ten out of ten.

The deployment is quite easy. It takes a few minutes. I rate the ease of deployment a seven out of ten.

We have already implemented some pipelines on Azure, but it's not similar to what Machine Learning Studio offers. People who want to start using the product must read the box. Some things are not easy to implement. We are only using Azure. Overall, I rate the tool 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.