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

Let's compare AutoML and JetFerry.ai

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