Free sections from Ultra Transcenders AI-300: Operationalizing Machine Learning and Generative AI Solutions, on the comparisons and decisions the exam keeps asking about.
When to use a workspace, a registry, models, environments, components and data assets.
uri_file, uri_folder and mltable data assets, and how mltable.load() finds the MLTable file.
Experiments, runs, parameters, metrics, artifacts and autologging.
Search spaces, sampling methods and early termination policies.
Blue-green deployments, traffic splitting, mirroring and instant rollback.
Where prompts are processed for each deployment type, and which one meets data residency needs.
When to use provisioned deployments, how 429s and spillover work, and how PTUs are billed.
Groundedness, relevance, coherence, fluency and risk and safety evaluators, and what each needs.