Microsoft Azure Machine Learning Studio Reviews

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. 

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 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.

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 use Microsoft Azure Machine Learning Studio primarily to develop small-scale machine learning models in the UI and later deploying them to the vendor for machine learning purposes.

I find Microsoft Azure Machine Learning Studio advantageous because it allows integration with Titan Scratch and offers an easy-to-use drag-and-drop menu for developing machine learning models. In my experience, I haven't identified any specific features that need improvement. I appreciate its simplicity and prefer it not to become overly complicated. For more sophisticated tasks, I would turn to other solutions like DataBricks, but for simplicity and ease of use, Azure Machine Learning Studio works well for me.

In future releases, I would like to see better integration with Power BI within Microsoft Azure Machine Learning Studio. This full integration would enhance the overall functionality and usability of the solution, creating a seamless experience for users.

I have been using Microsoft Azure Machine Learning Studio for the last six years. 

On a scale from one to ten, I would rate the stability a solid ten. From my personal perspective and experience, it has been extremely stable and reliable.

As for scalability, I would rate it a six. While it meets my current needs and expectations, there is room for improvement in terms of scalability for larger or more complex projects. However, considering that Azure Machine Learning Studio is designed as a compact and versatile tool, I don't have high expectations for extensive scalability beyond its current capabilities.

In general, Microsoft is responsive to community feedback, which is positive. However, their first-line support can be quite frustrating and is often considered a disaster. Dealing with the initial support team can be time-consuming and unproductive, as they often lack knowledge about the product or the specific issue being addressed Microsoft should implement better protocols to quickly escalate issues to higher-tier support with more expertise and knowledge about the product.

Neutral

The pricing for Microsoft products can be complex due to changes and being cloud-based, so it's not straightforward. I've been familiar with it for years, but sometimes details about product licenses and distribution can be unclear. For Microsoft Azure Machine Learning Studio specifically, I would rate the price a six out of ten.

I would recommend Microsoft Azure Machine Learning Studio, depending on the problem you're trying to solve. For our organization, we've seen benefits in marketing, particularly in calculating customer lifetime value. It's useful because it doesn't require much time to develop and provides immediate business results. I would rate it an eight out of ten.

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.