H2O Sparkling Water vs IBM Watson Machine Learning (Legacy)

Compare H2O Sparkling Water vs IBM Watson Machine Learning (Legacy) based on verified reviews from real users in the Data Science and Machine Learning Platforms market. H2O Sparkling Water has a rating of 4.9 stars with 15 reviews while IBM Watson Machine Learning (Legacy) has a rating of 4.25 stars with 13 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
4.9
(15 reviews)
4.25
(13 reviews)
Ratings Distribution
 
 
 
 
 
 
 
 
 
 
Willingness to recommend
67% Yes
Would Recommend
73% Yes
Would Recommend

Product Capabilities

Overall Capability Score
Overall Capability Score
 
 
 
 
 
4.67
Overall Capability Score
 
 
 
 
 
4.15
Data Exploration and Visualization
Data Exploration and Visualization
 
 
 
 
 
5
Data Exploration and Visualization
 
 
 
 
 
4.11
Platform and Project Management
Platform and Project Management
 
 
 
 
 
5
Platform and Project Management
 
 
 
 
 
4.33
Performance and Scalability
Performance and Scalability
 
 
 
 
 
5
Performance and Scalability
 
 
 
 
 
4.5
Data Access
Data Access
 
Data Access
 
 
 
 
 
4.29
Data Preparation
Data Preparation
 
Data Preparation
 
 
 
 
 
4.17
Augmentation (Automation)
Augmentation (Automation)
 
Augmentation (Automation)
 
 
 
 
 
3.75
User Interface
User Interface
 
User Interface
 
 
 
 
 
4.25
Machine Learning
Machine Learning
 
Machine Learning
 
 
 
 
 
3.88
Other Advanced Analytics
Other Advanced Analytics
 
Other Advanced Analytics
 
 
 
 
 
4
Flexibility and Openness
Flexibility and Openness
 
Flexibility and Openness
 
 
 
 
 
4.14
Delivery
Delivery
 
Delivery
 
 
 
 
 
4.43
Model Management
Model Management
 
Model Management
 
 
 
 
 
4.5
Pre-canned Solutions
Pre-canned Solutions
 
Pre-canned Solutions
 
 
 
 
 
4
Collaboration
Collaboration
 
Collaboration
 
 
 
 
 
4.43
Coherence
Coherence
 
Coherence
 
 
 
 
 
4.29

Customer Experience

Evaluation & Contracting
Evaluation & Contracting
 
 
 
 
 
4.79
Evaluation & Contracting
 
 
 
 
 
4.22
Pricing Flexibility
Pricing Flexibility
 
 
 
 
 
4
Pricing Flexibility
 
 
 
 
 
3.8
Ability to Understand Needs
Ability to Understand Needs
 
 
 
 
 
4
Ability to Understand Needs
 
 
 
 
 
4.4
Integration & Deployment
Integration & Deployment
 
 
 
 
 
4.8
Integration & Deployment
 
 
 
 
 
4.18
Ease of Deployment
Ease of Deployment
 
 
 
 
 
4
Ease of Deployment
 
 
 
 
 
4.33
Quality of End-User Training
Quality of End-User Training
 
 
 
 
 
4
Quality of End-User Training
 
 
 
 
 
4
Ease of Integration using Standard APIs and Tools
Ease of Integration using Standard APIs and Tools
 
 
 
 
 
4
Ease of Integration using Standard APIs and Tools
 
 
 
 
 
4.2
Availability of 3rd-Party Resources
Availability of 3rd-Party Resources
 
 
 
 
 
4
Availability of 3rd-Party Resources
 
 
 
 
 
3.8
Service & Support
Service & Support
 
 
 
 
 
4.87
Service & Support
 
 
 
 
 
4.08
Timeliness of Vendor Response
Timeliness of Vendor Response
 
 
 
 
 
4
Timeliness of Vendor Response
 
 
 
 
 
4.4
Quality of Technical Support
Quality of Technical Support
 
 
 
 
 
4
Quality of Technical Support
 
 
 
 
 
4
Quality of Peer User Community
Quality of Peer User Community
 
 
 
 
 
4
Quality of Peer User Community
 
 
 
 
 
3.67

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.