Let's compare AutoML and JetFerry.ai

Platform H2O / AutoML JetFerry.ai
What is it? Distributed in-memory machine learning platform with linear scalability Cloud-based ML Delivery platform
How does it work? "Based on Supervised Learning - a bunch of models
(with either random initialization and/or different subsets of the training set),
and taking their average as the result."
Training Data (based on extensive data attribution)
|
Multiple Training Data Sets
|
Multiple Classifiers
|
Combine Classifiers
|
Evaluate (and iterate)
"Based on Supervised Learning - a bunch of models
(with either random initialization and/or different subsets of the training set),
and taking their average as the result."
Training Data (based on extensive data attribution)
|
Multiple Training Data Sets
|
Multiple Classifiers
|
Combine Classifiers
|
Evaluate (and iterate)
Resources Required Data Scientist Team
DevOps / IT team
Multi-node clusters / Dedicated Hardware
None
None
None
Typical Prep time 3-6 months 1 month
Typical Implementation time 6-9 months modeling (WFH extends time) 2-5 months
Best use-case Want best-available classification, not worried about time Fastest ROI and turn-around time, with no false starts or experiments
Performance Tradeoff (Accuracy) N models : (speed) roughly N times slower Cost vs Time
Key Advantages Multiple components across teams
Customized approach
In-house teams, tighter control
"White-box" AI - focus on seeing and understanding rather than hiring and retaining
Proven methodology, get regular, reliable updates
Single Integrated platform for descriptive + predictive analytics + prescriptive analytics
Key Success Criteria Data Scientist Team dynamics
Methodology
Integrated processes
Wider and deeper knowledge-base
4Cs Methodology
Systems integration

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