IBM Watson Machine Learning (Legacy) vs Infosys Information Platform

Compare IBM Watson Machine Learning (Legacy) vs Infosys Information Platform based on verified reviews from real users in the Data Science and Machine Learning Platforms market. IBM Watson Machine Learning (Legacy) has a rating of 4.25 stars with 13 reviews while Infosys Information Platform has a rating of 4 stars with 4 reviews. See side-by-side comparisons of product capabilities, customer experience, pros and cons, and reviewer demographics to find the best fit for your organization.
Overall Peer Rating
4.25
(13 reviews)
4
(4 reviews)
Ratings Distribution
 
 
 
 
 
 
 
 
 
 
Willingness to recommend
73% Yes
Would Recommend
50% Yes
Would Recommend

Product Capabilities

Overall Capability Score
Overall Capability Score
 
 
 
 
 
4.15
Overall Capability Score
 
 
 
 
 
3.75
Data Exploration and Visualization
Data Exploration and Visualization
 
 
 
 
 
4.11
Data Exploration and Visualization
 
 
 
 
 
4.25
Platform and Project Management
Platform and Project Management
 
 
 
 
 
4.33
Platform and Project Management
 
 
 
 
 
3.75
Performance and Scalability
Performance and Scalability
 
 
 
 
 
4.5
Performance and Scalability
 
 
 
 
 
4
Data Access
Data Access
 
 
 
 
 
4.29
Data Access
 
Data Preparation
Data Preparation
 
 
 
 
 
4.17
Data Preparation
 
Augmentation (Automation)
Augmentation (Automation)
 
 
 
 
 
3.75
Augmentation (Automation)
 
User Interface
User Interface
 
 
 
 
 
4.25
User Interface
 
Machine Learning
Machine Learning
 
 
 
 
 
3.88
Machine Learning
 
Other Advanced Analytics
Other Advanced Analytics
 
 
 
 
 
4
Other Advanced Analytics
 
Flexibility and Openness
Flexibility and Openness
 
 
 
 
 
4.14
Flexibility and Openness
 
Delivery
Delivery
 
 
 
 
 
4.43
Delivery
 
Model Management
Model Management
 
 
 
 
 
4.5
Model Management
 
Pre-canned Solutions
Pre-canned Solutions
 
 
 
 
 
4
Pre-canned Solutions
 
Collaboration
Collaboration
 
 
 
 
 
4.43
Collaboration
 
Coherence
Coherence
 
 
 
 
 
4.29
Coherence
 

Customer Experience

Evaluation & Contracting
Evaluation & Contracting
 
 
 
 
 
4.22
Evaluation & Contracting
 
 
 
 
 
4.5
Pricing Flexibility
Pricing Flexibility
 
 
 
 
 
3.8
Pricing Flexibility
 
 
 
 
 
4.5
Ability to Understand Needs
Ability to Understand Needs
 
 
 
 
 
4.4
Ability to Understand Needs
 
 
 
 
 
4.5
Integration & Deployment
Integration & Deployment
 
 
 
 
 
4.18
Integration & Deployment
 
 
 
 
 
4.5
Ease of Deployment
Ease of Deployment
 
 
 
 
 
4.33
Ease of Deployment
 
 
 
 
 
4.5
Quality of End-User Training
Quality of End-User Training
 
 
 
 
 
4
Quality of End-User Training
 
 
 
 
 
4.5
Ease of Integration using Standard APIs and Tools
Ease of Integration using Standard APIs and Tools
 
 
 
 
 
4.2
Ease of Integration using Standard APIs and Tools
 
 
 
 
 
4.5
Availability of 3rd-Party Resources
Availability of 3rd-Party Resources
 
 
 
 
 
3.8
Availability of 3rd-Party Resources
 
 
 
 
 
5
Service & Support
Service & Support
 
 
 
 
 
4.08
Service & Support
 
 
 
 
 
4.25
Timeliness of Vendor Response
Timeliness of Vendor Response
 
 
 
 
 
4.4
Timeliness of Vendor Response
 
 
 
 
 
4
Quality of Technical Support
Quality of Technical Support
 
 
 
 
 
4
Quality of Technical Support
 
 
 
 
 
4
Quality of Peer User Community
Quality of Peer User Community
 
 
 
 
 
3.67
Quality of Peer User Community
 
 
 
 
 
4.5

Product Review Excerpts

Favorable Review Excerpts
Critical Review Excerpts

Reviewer Demographics

Reviewer Demographics by Company Size
Reviewer Demographics by Industry

Reviewer Considerations

Other Vendors Considered by Reviewers

"Willingness to Recommend" is calculated based on the responses to the question "Would you recommend this product to others?" The options include "yes," "yes, with reservations," "I do not know" and "no." The percentage is calculated as number of "yes" responses divided by total responses for the question.

"Favorable" and "Critical" user reviews are selected using the review helpfulness score. The helpfulness score predicts the relative value a user receives from a given review based on a number of factors. Factors may include the content in the review, feedback provided by other readers, the age of the review, and other factors that indicate review quality. The favorable review displayed is selected from the most helpful 4 or 5 star review. The critical user review displayed is selected from the most helpful 1,2 or 3 star review.