Microsoft Azure Machine Learning Studio Reviews

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. 

We primarily use the solution for sales forcasting and for creating a pipeline in Azure. We are publishing the pipeline from Azure DevOps, and through the AML endpoint so that the pipeline will run one after the other models. These predictions will be stored and we can visualize everything. 

The designer and notebooks are great. We like the pipelines we are able to deploy and the process is very simple.

The visualizations are great. It makes it very easy to understand which model is working and why.

The setup is simple. 

It is stable and reliable.

I have had no trouble scaling.

Technical support is good. 

The solution cannot connect to private block storage. It does not allow this connection, which is a pain point. The confidential data needs to be removed from the block, and that becomes a security issue. 

In Azure Databricks, how we are promoting the models could be easier. The UI in Daabricks is a bit easier. We'd like ML Studio to be streamlined. 

I've used the solution for about two and a half years. 

The solution is stable and reliable. There are no bugs or glitches. It doesn't crash or freeze. The performance is good. 

The solution can scale. I haven't used Azure Kubernetes services yet. However, I haven't had issues with scaling so far. 

We have around ten to 20 people on our project using the solution. Many users use it in our company - not just on my team.

I've reached out to technical support. They have SLAs in place that help us to troubleshoot issues. Even critical issues get sorted out quickly. We're using premium Microsoft technical support. 

We also use Databricks. In Databricks, there is no designer module to design pipelines. There are other features available. 

They do behave in the same way; however, in Databricks, I do need to do more configurations and a bit more work with it. Still, it allows me to connect to private blocks, which I cannot do in this product. It also requires me to run job clusters separately. 

Security-wise, Databricks is more secure. 

This is easy to deploy. I did not fid the process to be overly complex. 

The solution has a higher price. I'd rate it three out of ten in terms of affordability. 

I am an end user. 

I'd rate the solution eight out of ten. I'm pretty happy with its capabilities. 

I use it for forecasting solutions, and building, deploying, and managing machine learning models.

One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option. As designers, we have the flexibility to leverage end-to-end features without having to code everything manually. Additionally, the platform provides convenient options for managing email operations. I appreciate the extensible AI feature; it effortlessly generates a report even in the absence of explicit report instructions.

There's room for improvement in terms of binding the integration with Azure DevOps. I find the process somewhat intricate, especially when connecting to the issue-tracking system. Numerous steps and configurations need to be set up before effectively utilizing Azure DevOps. When it comes to the Home Office Machine Learning suite, I believe it would be more beneficial if there were shared capabilities for internet projects.

I have been working with it for one year.

The stability is impeccable. I would rate it ten out of ten.

I would rate its scalability capabilities nine out of ten. Ten users utilize it on a daily basis.

I'm dissatisfied with the technical support; they failed to offer the correct solution. I would rate their expertise four out of ten.

Neutral

The initial setup was fairly straightforward. I would rate it seven out of ten.

The deployment was completed within a week by following the guidebook. The in-house implementation was done by one individual. Maintenance is handled by a single individual who monitors the logs.

Overall, I would rate it 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.

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.

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.