Microsoft Azure Machine Learning Studio Reviews

We have data from our business, and we want to make AI models. The question is how we want to use those models in our business. That's what we're going to do next year.

The product is well organized. The thing is how we will get the models to work within our code. We have some suggestions there, but we want to gain more experience and be ready to answer that because we are currently working on this and don't have all the answers yet.

The tool is well organized. What I am very happy about is the ease of deploying new resources. You can easily create your pipeline within minutes.

One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. 

The tool should keep on updating new algorithms and not stay static. 

I have been working with the product for ten years. 

I rate Microsoft Azure Machine Learning Studio's stability as nine out of ten. 

I rate the solution's scalability a ten out of ten. I am the single user of Microsoft Azure Machine Learning Studio. 

We haven't had any experience with the tool's support because we didn't use it. We are mature developers and don't need it at this time. We don't have any complex business needs.

The tool's deployment time depends on the resource you will deploy. Some resources are deployed within minutes, while others may take more than 15-20 minutes. I have deployed mostly web applications, REST APIs, and databases.

We're trying to provide robust solutions to our customers, which previously involved multiple steps. Now, we're going to provide it in one step. That is our benefit because the customer will get a final solution, not a solution in steps. We will formalize and streamline them to align with our new solutions.

We pay only the Azure costs for what we use, which involves some subscription costs. But essentially, you pay for what you use. There are no extra costs in addition to the standard licensing fees.

We are trying to find some commercial value. I have learned how to use it, and we will integrate it into the project. That's our next goal.

I rate Microsoft Azure Machine Learning Studio a ten out of ten. If you want to use it, get the certifications, and then work on some projects to gain more experience.

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.

My company uses Microsoft Azure Machine Learning Studio to help our company's customers view AI solutions.

My company's clients' use cases will be that they use the solution to feed information to the system about their customers who purchase from them. The solution also helps one to combine products to engage in cross-selling and upselling activities while keeping track of customer lifetime value. The solution also helps its users with the pricing simulation part to figure out what prices are good for the business and maximize the closing of the sale.

The most valuable feature of the solution is the availability of ChatGPT in the solution.

Improvement will be possible with more machine learning functionalities in Microsoft Azure Machine Learning Studio since, at times, the current accuracy of the solution is not good enough. It would be good if Microsoft Azure Machine Learning Studio could have a generative AI tool similar to ChatGPT.

I have been using Microsoft Azure Machine Learning Studio for three years. My company functions as a reseller and a partner of Microsoft.

Stability-wise, I rate the solution a seven out of ten. With ML, you may face some data-related issues, especially considering that when dealing with customers at times, the data that comes in might not be clean. Stability-wise, you may face certain problems when you fail to refresh the data in the solution.

Scalability-wise, I rate the solution a seven out of ten. My company still has to do some of our own optimizations to the data part of the solution until and unless we subscribe to some third-party data lake services, which is a better option but comes at a higher cost.

My company's client's organization has around 10 to 50 users of the solution.

My company caters to the requirements of medium and enterprise-sized companies.

I rate the technical support a six out of ten.

Neutral

I rate the initial setup phase of the solution a six on a scale of one to ten, where one is difficult, and ten is easy. The initial setup phase of the solution was a bit complex. The setup phase is a bit difficult if you want to view Microsoft Azure Machine Learning Studio as an application.

The solution is deployed 50 percent on the cloud and 50 percent on-premises.

Considering the fact that my company currently builds some standard solutions, Microsoft Azure Machine Learning Studio's deployment takes us around two to three months.

I rate the solution's pricing a four on a scale of one to ten, where one is cheap, and ten is expensive.

There are some additional payments to be made apart from the licensing fees of the solution since buying Microsoft Azure Machine Learning Studio alone won't make it a complete solution. You will need the database and data lake services.

Microsoft Azure Machine Learning Studio does not allow users to have a PnP option, like an ERP or a CRM system, where everything works if you include the data with the system. Sometimes, it is difficult to generate good patterns using the solution. You need to have good experience with the solution to move around with the data from the beginning before coming up with different strategies to end different problems. In general, the product is not a straightforward solution.

There is a need for Microsoft Azure Machine Learning Studio's users to put in some programming efforts to make the solution work accurately under different scenarios.

I rate the overall solution a six out of ten.

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 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 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.

We use the solution to develop prompt flows.

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

The product must improve its documentation.

I have been using the solution for six months.

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

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

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

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

In terms of use case, we implement Microsoft Azure Machine Learning Studio using Python libraries, so basically, we have a centralized studio where we just have to drag and drop features and create the model out of the data that we have. Microsoft Azure Machine Learning Studio is pretty easy to use even for people who don't know much about coding. They just need to know the features and libraries, so it's similar to Tableau and Alteryx because users can drag and drop features to create models or pipelines. We create and deploy pipelines through Microsoft Azure Machine Learning Studio.

What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use.

Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it.

Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it.

What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners.

I've used Microsoft Azure Machine Learning Studio in the past year in my previous company, though I'm unsure about which version I was using at the time.

The functionality of Microsoft Azure Machine Learning Studio, specifically its underlying computing power, was managed by Azure, so stability-wise, it's a good solution.

Microsoft Azure Machine Learning Studio is a scalable tool. My previous company was on a volume-based model with it, and even if the data is large, it's easy to scale.

The company decided to go with Microsoft Azure Machine Learning Studio because of the partnership with Azure Cloud, so it's a way to leverage all features. The data was also hosted on the Azure platform, which made it pretty straightforward to use Microsoft Azure Machine Learning Studio rather than integrate with other tools.

Setting up Microsoft Azure Machine Learning Studio was very easy and is comparable to how easy it is to use any feature available in the tool.

Configuring the pipeline takes just ten to fifteen minutes, but that would still depend on the data volume.

My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it.

Approximately two hundred to three hundred people, mostly part of the data analytics team, were using Microsoft Azure Machine Learning Studio within the company.

My advice to anyone using Microsoft Azure Machine Learning Studio for the first time is to have an understanding of machine learning, deep learning, and libraries. You should also know the scripts because features are created on top of the machine learning libraries used in Python. If you want more optimizations or a better accuracy rate, you need a proper understanding of machine learning or a machine learning background before using Microsoft Azure Machine Learning Studio.

I'm rating Microsoft Azure Machine Learning Studio eight out of ten because it still needs some improvement. For example, because the drag-and-drop feature of the tool was written or based on Python, when you're creating a model in Microsoft Azure Machine Learning Studio, you'll get good accuracy by writing the script in Python, so accuracy isn't standard. You can customize your metrics to get good accuracy, but what you'll get is completely generalized, so whatever use case you feed into the pipeline, it'll create a test to get good accuracy, but it'll not give you a guarantee that this will be the only accuracy you'll get.

The previous company I worked in was a partner of Microsoft Azure Machine Learning Studio.

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.