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Microsoft Azure Machine Learning Studio pros and cons

Vendor: Microsoft
3.8 out of 5
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Microsoft Azure Machine Learning Studio Pros review quotes

N Kumar - PeerSpot reviewer
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.
it_user1050483 - PeerSpot reviewer
Nov 13, 2019
The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure.
CS
Nov 12, 2020
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.
Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: April 2024.
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Mahendra Prajapati - PeerSpot reviewer
Sep 9, 2022
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.
it_user833565 - PeerSpot reviewer
Mar 8, 2018
MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse.
CO
Mar 13, 2024
The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow.
SaurabhSingh4 - PeerSpot reviewer
Mar 27, 2024
The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
CP
May 4, 2020
The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.
William Foo - PeerSpot reviewer
Aug 28, 2023
The most valuable feature of the solution is the availability of ChatGPT in the solution.
SD
Nov 6, 2020
It's a great option if you are fairly new and don't want to write too much code.
 

Microsoft Azure Machine Learning Studio Cons review quotes

N Kumar - PeerSpot reviewer
Jul 24, 2022
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.
it_user1050483 - PeerSpot reviewer
Nov 13, 2019
If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice.
CS
Nov 12, 2020
In terms of improvement, I'd like to have more ability to construct and understand the detailed impact of the variables on the model. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" it wasn't great.
Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: April 2024.
768,578 professionals have used our research since 2012.
Mahendra Prajapati - PeerSpot reviewer
Sep 9, 2022
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.
it_user833565 - PeerSpot reviewer
Mar 8, 2018
Enable creating ensemble models easier, adding more machine learning algorithms.
CO
Mar 13, 2024
One area where Azure Machine Learning Studio could improve is its user interface structure.
SaurabhSingh4 - PeerSpot reviewer
Mar 27, 2024
In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions.
CP
May 4, 2020
Integration with social media would be a valuable enhancement.
William Foo - PeerSpot reviewer
Aug 28, 2023
Stability-wise, you may face certain problems when you fail to refresh the data in the solution.
Vijay Rameshkumar - PeerSpot reviewer
Sep 1, 2023
In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform.