| 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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