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 stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"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."
"Watson Studio is very stable."
"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."
"It has a lot of data connectors, which is extremely helpful."
"The most valuable feature of Azure Machine Learning Studio for me is its convenience. I can quickly start using it without setting up the environment or buying a lot of devices."
"The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model."
"It helps in building customized models, which are easy for clients to use."
"It's easy to deploy."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"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."
"We would like to see it more web-based with more functionality."
"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"
"The initial setup was complex."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"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."
"The solution's interface is very slow at times."
"I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system."
"I would like to see modules to handle Deep Learning frameworks."
"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"The speed of deployment should be faster, as should testing."
"The initial setup time of the containers to run the experiment is a bit long."
"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."
"It could use to add some more features in data transformation, time series and the text analytics section."
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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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