Учасник із 2025
Build Data Lakes and Data Warehouses on Google Cloud
Earned квіт. 12, 2026 EDT
Introduction to Data Engineering on Google Cloud
Earned квіт. 7, 2026 EDT
AI Boost Bites: Prompting like a Pro with Google Workspace
Earned квіт. 3, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned груд. 28, 2025 EST
While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.
This video covers prompt engineering fundamentals for effective AI communication. Learn a simple framework (Persona, Task, Context, Format) to craft clear prompts, getting better, faster results from Gemini in Google Workspace. Discover how to use natural language, be specific, and iterate for optimal AI assistance.
This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.