Good for large teams with large data reiterating on their data projects in various steps.
Collaboration is the key aspect in whatever projects we do at our organization. We have people from web development, data science, and ML …
Overview
What is IBM Watson Studio?
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…
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Popular Features
- Interactive Data Analysis (22)9.999%
- Visualization (22)9.999%
- Connect to Multiple Data Sources (22)8.080%
- Extend Existing Data Sources (22)7.979%
Reviewer Pros & Cons
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What is IBM Watson Studio?
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…
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- Free/Freemium Version
- Premium Consulting / Integration Services
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Product Details
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What is IBM Watson Studio?
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.
IBM Watson Studio Competitors
IBM Watson Studio Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Frequently Asked Questions
Amazon SageMaker and Azure Machine Learning are common alternatives for IBM Watson Studio.
Reviewers rate Visualization and Interactive Data Analysis and Interactive Data Cleaning and Enrichment highest, with a score of 9.9.
The most common users of IBM Watson Studio are from Enterprises (1,001+ employees).
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Reviews and Ratings
(220)Attribute Ratings
Reviews
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May 10, 2021
Why Use IBM Waston Studio?
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.
- Data ingestion
- Batch data processing
- Built-in connectors to Python
April 05, 2021
IBM Watson Studio on Cloud Pak for Data for students
March 30, 2021
IBM Watson Studio on Cloud Pak for Data Review
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.
February 08, 2021
IBM Watson Studio on Cloud Pak for Data Review
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.
February 04, 2021
Review of IBM services
It provides better user experience. All your data on cloud and does not take up space locally.
February 04, 2021
Early review of IBM Watson
I think they are very similar but IBM Watson is not good enough yet to pay for the services that I can already get from Jupyter Notebook.
January 13, 2021
Auto AI is a must have for every Data Analyst
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.
January 04, 2021
Great services for fast and effecient data analytics!
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.
December 18, 2020
Data scientist - as a beginner
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.
November 25, 2020
Review on IBM Watson
As it offers more features and can be used for several applications like AI,ML,DS etc.,
November 24, 2020
My first experience on Watson Studios
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 integrated manner • modern JavaScript development: npm-based packaging, typescript, phosphor.js, • clean model/view separation, • well separated public/private APIs, fully extensible by third parties, high performance and Design."
November 04, 2019
Watson Studio Review
We didn’t evaluate other products but we liked what we saw in Watson Studio.
February 25, 2019
IBM Watson Studio: Ideal for Rapid Data Science and ML POCs and Deployments with Watson
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.
February 22, 2019
Analytics for the Masses
They are close, but I feel Alteryx is more of an enhanced Jupyter capability, whereas WS is more of an enterprise solution for multiple teams
February 15, 2019
IBM Watson studios , our first steps
We did not use any other one so this would be hard for me to answer.
February 15, 2019
Great first impression and lot of opportunities with Watson studio
Not applicable
February 14, 2019
Watson Studio review
Less satisfied with Watson and lack of support.
February 14, 2019
Watson Studio - Data Science Platform
I am excited with the roadmap of Watson Studio incorporating SPSS Modeler in the offerings.
May 08, 2018
Watson vs. DATA
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.
April 17, 2018
I used to swear by IBM DSX
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.
April 16, 2018
Great resource for learning
- AWS
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
April 13, 2018
Watson Studio opinion
Watson Studio offers more capabilities and diversity in tools and services.
April 06, 2018
DSx, more for experimenting than experience.
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.
April 05, 2018
IBM Data Science Experience (DSx) for big data analysis
IBM DSx is more comprehensive and easy to use, IBM Data science experience has many connectors to the data source and guarantees the portability with your old projects.