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."The system's ability to take a look at data, segment it and then use that data very differently."
"It has a lot of data connectors, which is extremely helpful."
"It is a very stable and reliable solution."
"Stability-wise, it is a great tool."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"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."
"The graphical nature of the output makes it very easy to create PowerPoint reports as well."
"ML Studio is very easy to maintain."
"The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."
"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."
"The most valuable feature is data normalization."
"It's good for citizen data scientists, but also, other people can use Python or .NET code."
"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."
"Visualisation, and the possibility of sharing functions are key features."
"The decision making in their decision making feature is less good than other options."
"The initial setup was complex."
"We would like to see it more web-based with more functionality."
"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."
"It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs."
"So a better user interface could be very helpful"
"Some of the solutions are really good solutions but they can be a little too costly for many."
"I would like to see modules to handle Deep Learning frameworks."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"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."
"It could use to add some more features in data transformation, time series and the text analytics section."
"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."
"The price could be improved."
"I think it should be made cheaper for certain people…It may appear costlier for those who don't consider time important."
"Microsoft Azure Machine Learning Studio could improve in providing more efficient and cost-effective access to its tools for companies like mine."
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IBM Watson Studio is ranked 10th 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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