Microsoft Azure Machine Learning Studio Reviews

Our organization employs people with diverse professional backgrounds. We have sociology, mathematics, and statistics backgrounds. We employ these people within our data science team. They require a certain amount of programming skills.

The good thing about Azure Machine Learning is they have a drag and drop feature. You can use Azure Machine Learning designer for all of your data science teams.

Any non-programmer can adopt it. All he needs is statistics and data analysis skills.                                                                                             

I used Azure Machine Learning in a free trial and I had a complete preview of the service. A problem that I encountered was that I had a model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer. I didn't find any option to upload my model, so that I can create my own block and use it in Azure Machine Learning designer.

I believe this is a problem because sometimes you have your model created on some other device and you just have a file that you think can be uploaded to Azure Machine Learning and can be tested through a simple drag and drop tool.

We have been using Azure for three months. We have been exploring it for different use cases. 

I haven't used it long enough to have found any bugs in our current system. If there were bugs I would definitely report it on their website.

We didn't have any problems with the setup. It was pretty straightforward.

It's an easy tool. They have a good level of resources and we are pretty low with resources as far as data science is concerned.

Azure Machine Learning offers an opportunity for those who haven't been introduced to Azure programming. You can use the data analytics and their statistics skills to build and deploy data science solutions that can be beneficial for society and for different organizations.

I would rate it a seven out of ten.

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 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 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.

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.

We use Microsoft Azure Machine Learning Studio when we need to connect with the customer's data. We can connect easily, and fast, and test and train quickly. We have quick results.

The most valuable feature of Microsoft Azure Machine Learning Studio is the ease of use for starting projects. It's simple to connect and view the results. Additionally, the solution works well with other Microsoft solutions, such as Power Automate or SQL Server. It is easy to use and to connect for analytics.

Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me.

I have used Microsoft Azure Machine Learning Studio within the last 12 months.

The stability of Microsoft Azure Machine Learning Studio could improve. The solution is good for test development but it is not good for production environments.

Microsoft Azure Machine Learning Studio

I have used other solutions, such as Anaconda previously, and I prefer them over Microsoft Azure Machine Learning Studio. They are more stable.

The initial setup of Microsoft Azure Machine Learning Studio is easy.

We have one data scientist for the deployment and a data analyst for maintenance of the Microsoft Azure Machine Learning Studio.

I would recommend this solution for MPPs for fast production or deployments, but do not recommend the solution for production.

I rate Microsoft Azure Machine Learning Studio a five 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.