Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: November 2024.
Data Science Lead at a energy/utilities company with 51-200 employees
Real User
Top 10
2023-05-05T08:27:11Z
May 5, 2023
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.
Associate Director Of Technology at Virtusa Global
MSP
2022-07-24T07:12:33Z
Jul 24, 2022
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.
Full stack Data Analyst at a tech services company with 10,001+ employees
Real User
2021-06-28T08:12:22Z
Jun 28, 2021
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.
The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.
Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.
It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.Microsoft Azure Machine Learning Will Help You:
Rapidly build...
The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow.
The product supports open-source tools.
The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant.
One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option.
The solution facilitates our production.
ML Studio is very easy to maintain.
The most valuable feature of the solution is the availability of ChatGPT in the solution.
The product's standout feature is a robust multi-file network with limited availability.
Microsoft Azure Machine Learning Studio is easy to use and deploy.
The solution is scalable.
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.
Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful.
The visualizations are great. It makes it very easy to understand which model is working and why.
The solution is really scalable.
Their web interface is good.
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.
It's easy to deploy.
Auto email and studio are great features.
Their support is helpful.
The most valuable feature is its compatibility with Tensorflow.
Azure's AutoML feature is probably better than the competition.
The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps.
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.
The initial setup is very simple and straightforward.
It's good for citizen data scientists, but also, other people can use Python or .NET code.
The solution is very easy to use, so far as our data scientists are concerned.
The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.
It's a great option if you are fairly new and don't want to write too much code.
The interface is very intuitive.
The AutoML is helpful when you're starting to explore the problem that you're trying to solve.
Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills.
The solution is very fast and simple for a data science solution.
The UI is very user-friendly and that AI is easy to use.
The most valuable feature is data normalization.
The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.
The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure.
Visualisation, and the possibility of sharing functions are key features.