IBM Watson Studio on Cloud Pak for Data Review
March 30, 2021

IBM Watson Studio on Cloud Pak for Data Review

Li Rong | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with IBM Watson Studio on Cloud Pak for Data

The IBM Watson Studio is mainly used for one single department, the data science team. It mainly addresses the devops overhead of heavy jupyter notebooks and provides an integrated interface for people who are not familiar with infra and storage. It also provides a point of integration with other IBM services.
  • Sharing with team
  • GitHub integration
  • Free pricing plan if you want to try things out
  • Loading times can be slow
  • Tabs can be hard to navigate
  • not enough out of box examples
  • Made running experiments more streamlined
  • Reduced devops overhead
  • Sometimes does mean integration with things that are not on IBM harder
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.
IBM Watson studio on Cloud Pak for Data is well suited for medium sized teams. It allows for collaboration between technical and non-technical users. It is less suited for companies who already has large built production ML pipelines, as the cost of migration could be high and the initial overhead of learning the tools still remains

IBM Watson Studio Feature Ratings

Connect to Multiple Data Sources
8
Extend Existing Data Sources
6
Automatic Data Format Detection
6
MDM Integration
5
Visualization
7
Interactive Data Analysis
8
Interactive Data Cleaning and Enrichment
8
Data Transformations
8
Data Encryption
7
Built-in Processors
8
Multiple Model Development Languages and Tools
6
Automated Machine Learning
6
Single platform for multiple model development
8
Self-Service Model Delivery
9
Flexible Model Publishing Options
8
Security, Governance, and Cost Controls
5