Google Cloud · Generative AI Leader
Data and the ML lifecycle
Generative AI Leader · Lesson 2 of 14 · 9:47
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In this lesson
Prepare for the Google Cloud Generative AI Leader exam. In this lesson: the forms of data and what they mean for a business, structured and unstructured data, labeled and unlabeled data, data quality (accurate, complete, consistent, relevant) and accessibility (availability, cost, format), the five stages of the machine learning lifecycle, and the Google Cloud tools for each one: Pub/Sub, Cloud Storage, BigQuery, Dataflow, and Agent Platform (AutoML, Model Garden, Model Registry, Model Monitoring, Feature Store, Pipelines). Includes three original practice questions.
Preview the topics
- Cymbal Retail lists four data sources for a new gen AI project. Which one is unstructured data?
- Cymbal Retail merges the product data of two chains it acquired: one writes dates as day, month, year, the other as month, day, year, and weights appear in pounds and kilograms. Which data quality problem is this?
- Six months after launch, Cymbal Retail’s demand forecasting model grows less accurate, because shopping habits have changed. Which tool alerts the team when incoming data drifts away from the training data?