Distributed Training

Six model selection tips; four phases of ML adoption; ensembles (bagging/boosting/stacking); experiment tracking and data versioning challenges; ML debugging; distributed training (data/model/pipeline parallelism); AutoML (hyperparameter tuning, NAS, learned optimisers); offline evaluation baselines; evaluation methods (perturbation, invariance, directional expectation, calibration, confidence, slice-based/Simpson's paradox)