H2O.ai Certified Machine Learning case studies
H2O.ai Certified Machine Learning · Put your knowledge to work in context.
The H2O.ai Certified Machine Learning credential is a vendor certification aimed at data scientists, ML engineers, and analytics professionals who build and operate models on the H2O platform. It signals practical fluency with H2O-3 and Driverless AI rather than general statistics alone: you are expected to prepare data, train and tune models (including AutoML workflows), explain predictions, and move models into production responsibly. The H2O.ai Certified Machine Learning case studies are 5 cases and about 4,264 words.
1The 98.6% Accuracy That Declined Almost Nobody
A mid-size card issuer has 2.4 million historical applications spanning 30 months, of which 1.35% defaulted within 12 months. The team trains an H2O-3 GBM with balance classes=TRUE on a random 70/15/15 split with 5-fold cross-validation.
780 words, about 4 min2The ZIP Code That Ate the Cluster
A home-services marketplace wants to predict which quotes convert to booked jobs. The training file is a 9.2 GB CSV with 11.8 million rows and 58 columns, loaded on a 3-node H2O-3 cluster with 48 GB of RAM per node.
883 words, about 4 min3The Leaderboard Winner Nobody Can Serve
An airline loyalty team runs H2O-3 AutoML on 6.5 million members and 210 features to predict 90-day redemption. The run uses max runtime secs=3600 , max models=40 , nfolds=5 , the default sort metric, and seed 42.
836 words, about 4 min4Reason Codes Under Audit
A lender uses Driverless AI to build a model that recommends credit-limit decreases. Compliance requires that every declined account receive the top four reason codes in plain language, and that those reasons be 'the factors that most increased the likelihood of the decline'.
874 words, about 4 min5The Nightly Batch That Won't Fit
A subscription streaming service scores all 41 million subscribers nightly to decide who receives a retention offer. The model is a Driverless AI experiment exported as a MOJO.
891 words, about 4 min