Microsoft Azure Machine Learning Studio Reviews

In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio.

I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so  this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning.

As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly.

When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft.

I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine.

What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased.

I've been working with Microsoft Azure Machine Learning Studio for nearly two years now.

Microsoft Azure Machine Learning Studio is a stable solution. My company is already using it in production. At least customers use the recommendations from Microsoft Azure Machine Learning Studio in production, so the solution is quite stable, at least in cases developed by my company.

Microsoft Azure Machine Learning Studio is a solution that's easy to scale. It's pretty easy because it is hosted on Kubernetes, and there is an option in the portal where I can simply move my plan from standard to enterprise. The solution also has an automatic scaling option available because it is on Kubernetes, so it can scale automatically. I'm seeing that it's quite scalable. This has nothing to do with availability because it just runs in the background, and it is not customer-facing, but the output is customer-facing, so availability is a different case, but in terms of scalability, Microsoft Azure Machine Learning Studio is scalable.

The technical support team for Microsoft Azure Machine Learning Studio was pretty good, though I had to tailor the answers to my requirement, but would rate support a four out of five. Most of the questions my company had, more or less, the support team already experienced, so the team had answers readily available which means there wasn't a need to do a lot of R&D, so getting answers from technical support didn't take a lot of time.

In terms of setting up Microsoft Azure Machine Learning Studio, initially, when my company started, the documentation wasn't so good, but now it has improved. Provisioning the solution only takes a few clicks, so it's no big deal, but setting up the pipelines because no enterprise will have a single environment, you'll have to create multiple pre-production and end production environments, so moving my latest changes to the next environment was a bit of a challenge.

Many terminologies are now in the market such as DevSecOps, and MLOps, so that MLOps documentation was available initially, but it wasn't very explanatory, but now, there's a lot of improvement in the MLOps documentation and that will help me move and propagate my changes from one environment to another.

Microsoft has made improvements into the tutorials, especially on MLOps. Finding MLOps experts in the market was also very tough initially, so my company was trying to learn on the job and do it, so it took some thinking and time, but it's still good because you can learn on the job and do it, but you won't always have the luxury of time to learn it.

In terms of pricing, for any cloud solution, you should know the tricks of the trade and how to use it, otherwise, you'll end up paying a lot of money irrespective of the cloud provider, so at least for Microsoft Azure Machine Learning Studio pricing versus AWS, I would rate it three out of five, with one being the most expensive, and five being the cheapest. It could be cheaper, but you also have to be careful when choosing the plans, for example, consider the architecture and a lot of other factors before choosing your plan, if you don't want to end up paying more. If your cloud provider has an optimizer that seems to be available in every provider, that would keep alerting you in terms of resources not being used as much, then that would help you with budgeting.

We evaluated quite a lot of options. We compared Microsoft Azure Machine Learning Studio against Google Cloud and AWS solutions, and there were several others available in the market. I'm trying to recollect the names which we compared the solution with. We did the benchmarking, but we went with Microsoft Azure Machine Learning Studio because our clients and their data were on Azure, though that doesn't necessarily make you go with the solution. After all, you can pull the data from any other cloud as well. For our use case, however, we found many of the things were readily available and the learning curve for Microsoft Azure Machine Learning Studio compared to others was better and easier. We didn't have to search for experts in the market to hire them because we could have our in-house team learn and deliver the solution on the job.

Microsoft Azure Machine Learning Studio is a cloud-native solution. It's completely cloud-based.

My company has eight users of Microsoft Azure Machine Learning Studio.

My rating for Microsoft Azure Machine Learning Studio is seven 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.

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.

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.

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 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.

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.

The use cases actually depend on the client's requirements.

We have been working with multiple clients so they have their own use cases, they have their own problem areas, and based on their use cases, we use that platform.

One of the use cases is dealing with dealer churn.

It's easy to deploy. 

It has many features which help the person avoid delving into more technical things. It's more user-friendly from a user point of view.

The solution is stable.

Technical support is helpful.

It's highly scalable. Since it is on the cloud, you can expand the storage, you can expand the RAM, and all those things. The best thing is the scalability.

Technical support could improve their turnaround time. 

I've been using the solution for approximately a year now.

It's quite stable. There are no bugs or glitches. It doesn't crash or freeze. It's reliable and the performance is good. 

It's quite scalable. It's on the cloud which makes it quite scalable.

We tend to use it for medium-sized organizations. The number of users is around 10 to 15. They are mostly engineers. 

Microsoft technical support has been wonderful. They are helpful and supportive. That said, the turnaround time can be improved a bit. 

Positive

We have three people that can handle deployments. It takes about two months to deploy. 

We provide maintenance to our clients and only need one person to handle it. It's not too maintenance-intensive. 

I'm not aware of how much the solution costs. I don't handle any of the licensing. 

We're a customer and an end-user. 

We're using the latest version of the solution. 

I'd rate the solution an eight out of ten.