IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI lifecycle management and accelerates time to value with an open, flexible multicloud architecture.
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Posit
Score 9.2 out of 10
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Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
With my experience on Jupyter Notebook I think both are good and currently more comfortable with Watson Studio product. With Jupyter it's open source (free) is always good. "Lots of languages (50), data visualization with Seaborn, work with the building blocks in a flexible and …
Watson Studio was our choice in data management because its "all-in-one" packaging. Watson studio also stood out to us because it was more affordable and free for our organization to try out. We also greatly value the open source ecosystem Watson Studio has fostered.
The learning curve for DSX is smaller compared to other tools. The data science user base often has preferred tools that they have used previously which are often not DSX which makes adoption of DSX by trained data scientists harder than new users.
First, I have to deploy H2O myself. Then 4 paradigm cannot customize code and run customized code as easily as IBM DSX. Last, I should say AliPAI is a good alternative, but it's too expensive.
I wanted an environment that can support multiple users without any restrictions. Also, R-Studio does not provide a collaborative environment for multiple users. The Auto feature selection in the SPSS modeler is a good node in DSx which helps make statistical decisions on …
SPSS - Totally different approaches, SPSS UI is now a well-known name with a well-established user base who we consider aren´t going anywhere but Statistics.
Modeler - A proven analytical solution with capabilities to deal with huge datasets, scalability offers you now the …
When developing the use case we considered using a big data platform for developing the required analytics. After evaluating the alternatives and costs we considered that using a big data platform would be too expensive for the kind of studies we are developing in the company. …
The mix of proprietary and open-source benefits that DSx offers gives me more flexibility than any other options I have encountered. I have the custom program building capability of Anaconda with the built-in predictive models of SPSS Modeler. I have more visualization …
The IBM Data Science Experience enables data scientists to collaborate through projects, to which they can add notebooks, data, data connections, and other users they want to collaborate with. In Jupyter notebooks they can use Python, R, or Scala, when needed with Apache Spark, …