Microsoft Azure Machine Learning Studio Reviews

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. 

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.

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.

I usually order a machine for training my models. I build the machine myself to include various images for working with Python or IR. I upload my data and scripts to the cloud and run the training process.

I'm beginning to learn about Databricks, which is a framework that works on Azure, AWS, and GCP. It has more power than the Azure main infrastructure, so I'm starting to explore it for things such as training models. I like all the features that Azure's main infrastructure provides, so I don't have a preferred feature. I think many people will move to Azure Databricks in the future.

The price of the solution has room for improvement.

I have been using the solution for almost three years.

I don't have much experience with production environments since they are usually managed by DevOps rather than me when I deploy my work. However, I believe the solution is stable.

The solution is really scalable.

Microsoft has great technical support, which is really beneficial.

The initial setup depends on the developer's knowledge of machine learning models as to whether it is easy or difficult. With a good understanding of these models, then we can get to work quickly in the environment. With 20 years of experience in IT, making applications on legacy platforms and non-web platforms, I have found that Azure has a very good implementation. The platform is so comprehensive that it doesn't matter what kind of work we do, we can find the tools needed to get the job done. In comparison to what I was doing five years ago, Azure is a great platform and I really enjoy working with it.

We should allocate up to 12 percent of our project time to deployment. Depending on the complexity of the solution, we should expect to spend more time on deployment.

Some of our implementations are in-house and others are not. 

The solution cost is high.

I give the solution an eight out of ten.

I began using Azure three months ago, connecting my local Visual Code environment with the actual environment. This was a major improvement for me, as I can now work and run experiments on my local computer. I'm really pleased with how comfortable I am using Azure on all platforms.

The solution requires a minimum of one developer for maintenance. We need a DevOps developer and the tech lead to define the scope of the problems to be solved. The tech lead will provide guidance and oversight, while the DevOps developer will be responsible for implementing the solutions.

I enjoy working and have no difficulty in recommending Azure Machine Learning Studio to others, however, I recognize that there are many implementations utilizing AWS. AWS is a formidable competitor, so it is essential to be familiar with both solutions. Unfortunately, I have missed out on opportunities because I am not situated in the US. The environment is excellent, however, the large American market and the companies therein rely heavily on our work. This requires me to stay apprised of current developments, such as the widespread adoption of AWS, and learn how to use alternative platforms.

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.

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.

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.