H2O.ai Certified Machine Learning study guide
H2O.ai Certified Machine Learning · Work through your exam, one topic at a time.
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 study guide has 6 sections and about 2,344 words.
01Machine Learning Fundamentals and Model Evaluation
The Machine Learning Fundamentals and Model Evaluation domain checks whether you can frame a supervised problem, choose a validation design, and pick the metric that matches the business decision. Questions often contrast accuracy with precision/recall, ask why k-fold cross-validation is used, and probe leakage.
404 words, about 2 min02H2O Platform Architecture and Deployment (H2O-3, Driverless AI)
The H2O Platform Architecture and Deployment domain tests how H2O-3 and Driverless AI are structured, how clients talk to them, and how models leave the training environment. Expect questions on cluster components, REST communication, MOJO/POJO artifacts, and the difference between an H2O-3 AutoML run and a Driverless AI experiment.
417 words, about 2 min03Data Preparation and Feature Engineering
Data Preparation and Feature Engineering checks whether you can load data into H2O correctly, create useful predictors without leakage, and keep training and scoring schemas aligned. Parse data with h2o.importFile() and inspect column types.
373 words, about 2 min04Model Training, Tuning, and AutoML
Model Training, Tuning, and AutoML tests whether you can configure H2O-3 algorithms, run a defensible search, and use AutoML without mistaking the leaderboard for a production decision. H2O-3 provides GLM, GBM, XGBoost, DRF, Deep Learning, Stacked Ensembles, and other algorithms.
399 words, about 2 min05Model Interpretation and Explainability
Model Interpretation and Explainability tests whether you can explain global behavior, local predictions, and the limits of each explanation method in H2O-3 and Driverless AI. Global methods summarize the model over a population: variable importance, partial dependence, and global SHAP summaries.
370 words, about 2 min06Productionization, Scoring, and MLOps
Productionization, Scoring, and MLOps tests whether you can move a trained H2O model into scoring, keep it reproducible, and monitor it after deployment. H2O-3 can score inside the cluster with h2o.predict() , but production often exports a POJO or MOJO.
381 words, about 2 min