Machine Learning Engineering in Action

index

A

A/B testing 316 – 333

data collection 317 – 319

evaluating categorical metrics 329 – 333

evaluating continuous metrics 319 – 323

using alternative displays and tests 325 – 329

what not to do 319

acceptance testing 438 – 470

data consistency 439 – 446

dangers of data silo 445 – 446

feature stores 441 – 442

process over technology 442 – 445

training and inference skew 440 – 441

end user vs. internal use testing 453 – 460

biased testing 456 – 457

dogfooding 457 – 458

SME evaluation 459 – 460

fallbacks and cold starts 447 – 452

cold-start woes 450 – 452

leaning heavily on prior art 448 – 450

model interpretability 460 – 469

Shapley additive explanations 461 – 463

using shap 463 – 466, 469

ACID-compliant storage layer 442

active retraining ...