We performed a comparison between IBM Watson Studio and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"The scalability of IBM Watson Studio is great."
"The solution is very easy to use."
"The most important thing is that it's a multi-faceted solution. It's a kind of specialist, not a generalist. It can produce very specific information for the customer. It's totally different from Google or any search engine that produces generic information. It's specialty is that it's all on video."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"It has greatly improved the performance because it is standardized across the company."
"Stability-wise, it is a great tool."
"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."
"The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"The most valuable feature is its compatibility with Tensorflow."
"The graphical nature of the output makes it very easy to create PowerPoint reports as well."
"Their web interface is good."
"Microsoft Azure Machine Learning Studio is easy to use and deploy."
"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."
"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."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers."
"The decision making in their decision making feature is less good than other options."
"So a better user interface could be very helpful"
"I want IBM's technical support team to provide more specific answers to queries."
"The main challenge lies in visibility and ease of use."
"Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge."
"I think maybe the support is an area where it lacks."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"The solution's initial setup process is complicated."
"The price of the solution has room for improvement."
"It could use to add some more features in data transformation, time series and the text analytics section."
"They should have a desktop version to work on the platform."
"There should be data access security, a role level security. Right now, they don't offer this."
"The solution cannot connect to private block storage."
"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."
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IBM Watson Studio is ranked 11th in Data Science Platforms with 13 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 48 reviews. IBM Watson Studio is rated 8.2, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". IBM Watson Studio is most compared with Databricks, Azure OpenAI, Google Vertex AI, Amazon Comprehend and IBM SPSS Modeler, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and IBM SPSS Statistics. See our IBM Watson Studio vs. Microsoft Azure Machine Learning Studio report.
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