06How do you make ML experiments reproducible and manage models from experiment to production?▼mediumDatabricksMicrosoftGoogle2 repliesunlockedReproducibility is what divides an ML platform from a heap of notebooks. The signal is capturing the full provenance (data, code, config, metrics) and a registry that governs promotion. Here is what to track and why each piece counts.Open full answer →
38Design an ML experiment-tracking and analysis platform.▼hardMetaGoogleMicrosoft1 replies◆ premiumEvery team rebuilds a spreadsheet of training runs and then drowns in it. The interviewer wants the platform that ingests runs, params, metrics, and artifacts at high write volume, ties them together by lineage, and makes thousands of experiments comparable, which is a different system from a model registry.Open full answer →
