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)10.0100%
- Visualization (22)10.0100%
- Connect to Multiple Data Sources (22)8.080%
- Extend Existing Data Sources (22)8.080%
Pricing
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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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- FAQs
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 Automatic Data Format Detection and Visualization and Interactive Data Analysis highest, with a score of 10.
The most common users of IBM Watson Studio are from Enterprises (1,001+ employees).
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Reviews and Ratings
(221)Attribute Ratings
Reviews
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January 28, 2023
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 teams working on projects involving all three domains in a single project. IBM Watson Studio on Cloud Pak for Data is a great asset for the data science team where we work on individual tasks, collaborate with teammates, and share findings and insights. It has all the basic tools required for visualization and modeling the algorithm. The IBM cloud Pak has numerous examples for various use cases.
- The R studio which is very flexible more than any IDE.
- Testing and developing the model locally before finally publishing.
- Collaborating with teammates on same data.
- Watson Studio is a little complex for beginners to get started. Paid courses explain them well to beginners.
- Many features that small teams might not use.
- Collaboration
- R studio flexibility
- Pipelines
86.66666666666666%
8.7
100%
10.0
95%
9.5
95%
9.5
80%
8.0
- Improved productivity of teams with collaboration.
- Faster processing of data so less time taken for tasks.
- Better Delivery of end products.
May 10, 2021
Why Use IBM Waston Studio?
We used IBM Waston for learning and helping other fellow members learn some concepts of machine learning. We learned about IBM Waston through Coursera Specialization and then continue experimenting with IBM Cloud for some time. Whether it is using their services or storing objects in a bucket, it was an amazing experience.
- IBM Watson Services like speech to text, etc. are just some clicks away. You just need to specify some basic details like location etc and the resource will be ready for use.
- IBM DB2 engine is a fully managed relational database for all your needs.
- There are a lot of services available from which users can choose what suits his/her needs.
- In starting, I found navigating through different services a bit difficult and overwhelming.
- IBM dashboard should be redesigned to make it simple.
- Rest all looks good.
- Able to run Jupyter notebooks and code there.
- Image recognition and text to speech service saves our lot of time.
- Cloud Storage and Databases ease the work of managing a remote database.
- IBM Waston is a lot time saving.
- Machine learning services and resources have great accuracy.
- Documentation is really good.
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.
IBM Watson Studio on Cloud Pak for Data helps me bring in multiple data streams in Batch and streaming mode and helps me to run ETL processes and then run ML algorithms on top of the processed data. The beauty is I don't need to think about managing resources like CPU, storage, and processing elements and focus all my efforts on the data analytics.
- Data ingestion
- ETL processes
- Integration with Python notebooks for ML algorithms
- Support to run SQL queries on Cloud
- Support for streaming data
- Streaming data support
- Connecting with existing Hadoop systems
- Data visualization on top of the data
- Data analysis
- AutoML
- Data visualisation
- Data engineering
- ETL
- APIs for enterprise softwares like Salesforce, Google Analytics, etc.
65%
6.5
50%
5.0
55%
5.5
55%
5.5
55%
5.5
- Cheaper storage
- Faster data processing
- Faster data analysis
- Data ingestion
- Batch data processing
- Built-in connectors to Python