Microsoft Azure Machine Learning Studio Reviews

We use the solution to develop prompt flows.

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

The product must improve its documentation.

I have been using the solution for six months.

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

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

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

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

The first time that I used this tool was in a project related to bike usage in the city of Boston. This project was part of a course that I concluded some months ago. In this project I used components to read data, for exploratory analysis, for steps of data munging, to split data, select hyperparameters, and some machine learning algorithms. In some steps I needed to insert R modules to apply some data transformation.

The target of this exercise was to predict bike usage in a day.

With this tool we could have all benefits of a cloud environment, such as scalability and access to machine-learning applications. These features are very important when you have large datasets and critical applications.

• It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component.
• When you import the dataset you can see the data distribution easily with graphics and statistical measures.
• Easy to deploy and provide the project like a service.

For my project/exercise, this tools was perfect. I would like to see modules to handle Deep Learning frameworks.

No issues with stability.

No issues with scalability.

I didn’t need to use the support, but this tool has great documentation.

Nowadays I use Python (Anaconda and Jupyter Notebook) and R (RStudio) to create my solutions and machine-learning models.

It was very simple and straightforward. It is really simple to start building a project.

There are two kinds of licenses, Free and Standard.

Free

• 100 modules per experiment.
• 1 hour per experiment.
• 10GB storage space.
• Single Node Execution/Performance.

Standard – $9.99/seat/month (probably a data scientist)

• $1 per Studio Experimentation Hour. You will pay according to the number of hours your experiments run.
• Unlimited modules per experiment.
• Up to seven days per experiment, 24 hours per module.
• Unlimited BYO storage space.
• On-premises SQL data processing.
• Multiple Nodes Execution/Performance.
• Production Web API.
• SLA.

You will be able to create your machine-learning project and extract insights from it just by dragging and dropping components and adjusting some parameters. This tool is very user-friendly, so without a lot of programming skills you can build machine-learning projects. 

If you need more control over machine-learning modules you will need to add R or Python modules to create a customized machine-learning model.

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 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.

I use the solution to create a data flow and map all the databases or users.

The drag-and-drop interface is good.

The solution must increase the amount of data sources that can be integrated. Many customers have different types of data sources. The tool only supports seven out of ten data sources. The tool must increase the integration of data sources.

The tool is used to create flows. Its stability does not matter much as far as it creates the flow. Once we have created the flow, we just need to deploy it in our environment. Once the flow is defined, we put the algorithm in the machine learning node.

The product will be only used by a couple of people who design the flow and the model. There might be only three or four users in an organization with 100 employees.

The product is cloud-based.

The product is not that expensive.

Scalability is irrelevant to the tool. BFSI and IT companies use the product in India. Everyone is trying to leverage AI. The market is going towards AI. I see a lot of opportunity in it. The consumption of AI will increase in the future.

I will recommend the solution to my clients. We can support them because we are a partner with Microsoft. The solution enables customers to design flows using most of the available data sources. They can also create algorithms for predictive analysis. Overall, I rate the product a 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.

We're mainly using Microsoft Azure Machine Learning Studio to run experiments on our data for predictive analytics.

I like being able to compare results across different training runs. The hyperparameter tuning function is a valuable feature because it provides the ability to run multiple experiments at the same time and compare results.

The initial setup time of the containers to run the experiment is a bit long.

I've been using this solution for about a year.

It's pretty stable, and I have not had any issues. I would rate the solution's stability at nine out of ten.

Microsoft Azure Machine Learning Studio itself is not really designed to be deployed. You get the model output from Machine Learning Studio, and then you have to use other Azure services for deployment. Thus, it's not very scalable in that sense.

However, for scalability in terms of machine learning and running different algorithms, I would rate it at eight out of ten. In terms of deploying machine learning solutions, I would not rate it very high. I am the only one who uses this solution in my organization, and we are not planning to increase usage at present.

The initial setup wasn't too complex, and I would rate it at eight out of ten. The documentation was easy to follow.

The deployment took a couple of days. We obtained the data, made it available, and then set up the environment. We tried out different models and ran experiments.

We deployed it ourselves.

On a scale from one to ten, with ten being overpriced, I would rate the price of this solution at six.

If you want to train models on larger datasets, then Microsoft Azure Machine Learning Studio is a good solution. If you need to run a few diverse set of experiments with different environments, then it really comes in handy.

Overall, I would rate Microsoft Azure Machine Learning Studio at eight out of ten because it's easy to start using it. Also, it's pretty robust and stable, and the interface is nice to work with.

I use a combination of Microsoft Azure Machine Learning Studio and Azure Databricks. I mostly use Azure Databricks for building a machine learning system. There are several workflows for a machine learning tuning system that involves data pre-processing, quick modeling pipelines that execute within a couple of seconds, and complex model pipelines, such as hyperparameters. Additionally, there is a setting to set different AutoML parameters. 

For the training and evaluation phase of the whole machine learning system, I use MLflow, for a testing system and a model serving system, which is one core component of Databricks. I use it for Model Register and it allows me to do many things, such as registering model info, logs, and evaluation metrics.

The newer version of this solution has better integration with automated ML processes and different APIs. I feel like it is quite powerful in terms of general machine learning features, such as training data handily by having different sampling methods and has more useful modeling parameter settings. People who are not data scientists or data analysts, can quickly use the platform and build models to leverage the data to do some predictive models.

Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon. It has the most sophisticated set of categories of parameters. The data encodings and options are good and it has the most detailed settings for specifics models.

I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system.

The developers for this solution have not been as active in improving it as other solutions have had more improvements, such as Databricks.

Sometimes there might be some data drifting problems and this is what I am currently working on. For example, when our new data has a drift from the previous old data. I need to first work out a solution. Azure in Databricks or in Azure Machine Learning Studio both works fine. However, the normal data drifting solution is not working that well for the problem that I am facing. I am able to receive the distribution change and numerical metrics changes, but it will not inform me how to fix them.

I have been using this solution for approximately three months.

I use Databricks alongside this solution.

I rate Microsoft Azure Machine Learning Studio a seven out of ten.