IBM Watson Studio
IBM Watson Studio on Cloud Pak for Data
We used IBM Waston for learning and helping other fellow members learn some concepts of machine learning. We learned about IBM Waston …
Currently, I am a student and I do not have any idea how many students studying and practicing along with me are using IBM Watson Studio …
The IBM Watson Studio is mainly used for one single department, the data science team. It mainly addresses the devops overhead of heavy …
Storing data in the form of Worksheets and CSV files so that multiple users can use the data partially irrespective of location.
I am into mentoring services, so I teach and assist learners on this technology.
We mostly use IBM Watson Studio for its Notebook features for running Python codes. It allows us to work on the code together and generate …
Used to test prototype applications for clients. Mostly used for creating predictive data models, descriptive models, and basic ETL. There …
This system is currently being used with a few students on a data science degree within the School of Computing at our University. We are …
We primarily use IBM Watson Studio (formerly IBM Data Science Experience) as training for developing analytical skills in Coursera courses.
I am using IBM Watson studio for my personal interest. Since it is not involved a lot of programming, I am creating my own website with a …
IBM Watson studio is being used to host Juypter Notebooks. These notebooks contains analyses for various projects. The primary project …
I have been using IBM Watson [Studio (formerly IBM Data Science Experience)] for the purpose of Data science course which was offered by …
IBM Watson Studio [formerly IBM Data Science Experience] helps my business unit to make some business decisions concerning management of …
Currently I'm using IBM Watson as part of my Professional development via Coursera. IBM Watson to my knowledge isn't being used by my …
Interactive Data Analysis (21)
Connect to Multiple Data Sources (21)
Extend Existing Data Sources (21)
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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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Google Cloud may be a good place but it is not as easy to understand as IBM Watson is. Google Cloud has a lot of things and it is terrifying for a beginner. You need hours of specialization for that. On other hand, anyone can start using IBM Waston just by the following documentation.
AWS Sagemaker is a well-established product that supports on-demand notebooks, data pipelines, and so on, however, it also comes with the learning overhead of the whole AWS stack. It does allow per-defined models, but the benefit of using IBM Watson Studio is that users are able to leverage per-trained models and significantly reduce training time.
Organization of data, use of data, manage the data, visualize the data is easy. Use of the environment for any project. We can use python or R or Scala in the notebook. Data cleaning a remarkable feature of IMB Watson Studio. Deployment of ML models is easy. Monitoring the Models is also easy.
Easy to use, but still requires a lot of coding to use. There is no ranking of models used and models are not persistent, which means you have to keep running the models again every time you leave the session. The filesystem is clunky and need to keep authorizing Google Drive to save any datasets.
The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
IBM offers a deep neural network training workflow, with a flow editor interface similar to the one used in Azure ML Studio. However, the custom build modeling in IBM has notebooks such as Jupiter to program models manually using popular frameworks like TensorFlow, sci-kit-learn, PyTorch, XGboost, PMML, and IBM SPSS.
As it offers more features and can be used for several applications like AI,ML,DS etc.,
We didn’t evaluate other products but we liked what we saw in Watson Studio.
As an IBM Business Partner, we are financially incentivized to recommend and deploy IBM solutions where it makes sense to do so for the customer. Against other solutions, few have the governance and security that IBM offers, which is essential for any kind of work in highly regulated industries. IBM's solution may not be the sexiest, but it's the most bulletproof.
We did not use any other one so this would be hard for me to answer.
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.
AWS Sagemaker is new, and I personally think it's better than sliced bread. There's very little set up to do. Watson Studio needs to up its game against Sagemaker.
AWS stacks up very favourably against Watson Studio, and in fact this is what the customer ultimately chose over Watson Studio after an evaluation period due to the sophistication, maturity, security, and capabilities of the AWS components. The downsides of AWS are having to pay for every byte downloaded, and the steep learning curve. The advantages of the Watson Studio environment over AWS are: better support for hybrid deployments (not everything has to go in the cloud); ease of integration with other Watson APIs and components (e.g. NLU, Speech to Text, etc.), and cheaper usage costs
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.