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 is a very stable and reliable solution."
"The scalability of IBM Watson Studio is great."
"The system's ability to take a look at data, segment it and then use that data very differently."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"It is a stable, reliable product."
"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 has a lot of data connectors, which is extremely helpful."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"Microsoft Azure Machine Learning Studio is easy to use and deploy."
"Their support is helpful."
"Auto email and studio are great features."
"The UI is very user-friendly and that AI is easy to use."
"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."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"It's a great option if you are fairly new and don't want to write too much code."
"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."
"The decision making in their decision making feature is less good than other options."
"So a better user interface could be very helpful"
"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."
"I want IBM's technical support team to provide more specific answers to queries."
"Some of the solutions are really good solutions but they can be a little too costly for many."
"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."
"I think they should improve two things. They should make their user interface more user-friendly. Integration could also be better. Because Microsoft Machine Learning is a Microsoft product, it's fully integrated with Microsoft Azure but not fully supported for other platforms like IBM or AWS or something else."
"The solution should be more customizable. There should be more algorithms."
"The price of the solution has room for improvement."
"There should be data access security, a role level security. Right now, they don't offer this."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"The data cleaning functionality is something that could be better and needs to be improved."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
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 50 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.
See our list of best Data Science Platforms vendors and best AI Development Platforms vendors.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.