1Architecting low-code AI solutions

• 1.1BigQuery ML and AutoML

  Understand choosing the right BigQuery ML model by business problem (linear/binary classification, regression, time-series, matrix factorization, boosted trees, autoencoders), feature engineering and prediction with BigQuery ML, and AutoML (data prep/labeling/Tabular Workflows, custom/forecasting models on tabular data, configuring/debugging trained models). (Note: legacy AutoML Text is being deprecated in favor of Gemini-based models on Vertex AI; AutoML for image/video/tabular continues.)

• 1.2ML APIs, foundation models, and RAG

  Understand building applications with ML APIs from Model Garden, using industry-specific APIs (Document AI API, Retail API, etc.), and implementing retrieval augmented generation (RAG) applications with Vertex AI Agent Builder.