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."It has a lot of data connectors, which is extremely helpful."
"The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people."
"It is a stable, reliable product."
"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."
"IBM Watson Studio consistently automates across channels."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"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."
"The most valuable feature is data normalization."
"Visualisation, and the possibility of sharing functions are key features."
"When you import the dataset you can see the data distribution easily with graphics and statistical measures."
"The UI is very user-friendly and that AI is easy to use."
"The solution is really scalable."
"The product's standout feature is a robust multi-file network with limited availability."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"I want IBM's technical support team to provide more specific answers to queries."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"The solution's interface is very slow at times."
"I think maybe the support is an area where it lacks."
"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."
"So a better user interface could be very helpful"
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"The main challenge lies in visibility and ease of use."
"The initial setup time of the containers to run the experiment is a bit long."
"It would be great if the solution integrated Microsoft Copilot, its AI helper."
"In future releases, I would like to see better integration with Power BI within Microsoft Azure Machine Learning Studio."
"I would like to see modules to handle Deep Learning frameworks."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"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."
"n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces."
"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 53 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 Anaconda, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Amazon SageMaker. See our IBM Watson Studio vs. Microsoft Azure Machine Learning Studio report.
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