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.

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.

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.

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.

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.

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.

Microsoft Azure Machine Learning Studio can be used for developing models, such as predicting energy usage, as I did for my bachelor's project, where I predicted future energy usage for a city in Norway. The solution can also be used for classification tasks, such as identifying objects in images.

In terms of features, I personally find Azure to be clearer and better than Google because it provides better quality and clarity regarding what needs to be done.

The icons in the solution could be improved to include examples of how to use each container, as sometimes it's unclear which container to choose. It would be helpful to provide examples to understand better which virtual machine or how many courses to use. Overall, the icons in the solution could be improved to provide better guidance to users.

Additionally, the setup process for the solution could be made easier.

I have been using Microsoft Azure Machine Learning Studio for half a year. I am a student and user of the solution.

I think Microsoft Azure Machine Learning Studio is more stable than Google.

In terms of scalability, I believe that the solution is good. Although I have only used it for two projects, I think that it provides a good level of scalability. However, as I have only used it within my organization, I may not have experienced all of the possibilities that the solution offers.

Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful. It is often the case that everything one needs is already in the documentation, so I haven't had to use the support much. Even when I have reached out for support, I have always received a prompt response.

The initial setup for me was initially quite complex, but after completing a course related to Microsoft Azure Machine Learning Studio, it became less complex. However, one needs to have a good understanding of the required parameters and what the model needs to do in order to achieve good performance. So sometimes, it's not that simple. The deployment process took me a couple of hours to complete. I was able to do it quickly because I was using Azure Machine Learning Designer and Python SDK while also learning automation. The setup process for AltaML was easy and could be completed in hours. With Python SDK, the setup process was quite long because of the code that needed to be written, so one needs to know what to write.

I used the free student license for a few months to operate the solution, but I'll have to pay for it if I want to do more now.

Before choosing Microsoft Azure Machine Learning Studio, I only evaluated Google Cloudpath.

If you plan to use this solution, I suggest you not be intimidated by its complexity at first. You will gain more clarity regarding the solution over time with perseverance and practice. Overall, I rate the solution 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.