Gemini in BigQuery for Data Practitioners
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Learning Track
Generative AI
Delivery methods
On-Site, Virtual
Duration
1 day
This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
Learning Objectives
- Define the features of Gemini in BigQuery that aid the data-to-AI pipeline.
- Explore data with Insights and Table Explorer.
- Develop code with Gemini assistance.
- Discover and visualize workflow with data canvas.
- Explain the workflow of using AI/ML models for predictive and generative tasks in BigQuery.
- Create a solution for leveraging Gemini models in BigQuery with SQL queries and Jupyter Notebooks.
Who Should Attend
Data analysts, data engineers, and other data professionals who wish to use Gemini in BigQuery to boost productivity and understand their unstructured data.
Prerequisites
Prior experience with programming languages including SQL and/or Python. • Basic knowledge of ML and generative AI.
Course outline
- Gemini on Google Cloud
- Overview of Gemini on BigQuery
- Introduction to course use case
- Data exploration and preparation
- Insights
- Table Explorer
- Lab: Explore Data with Gemini in BigQuery
- Gemini for writing code
- Troubleshooting and testing with Gemini
- Prompting best practices
- Lab: Develop Code with Gemini in BigQuery
- Introduction to Data Canvas
- Data Canvas capabilities
- Prompting best practices for Data Canvas
- Lab: Use Data Canvas to Visualize and Design Queries
- BigQuery ML
- Using Gemini in your SQL queries
- Gemini in BigQuery Notebooks
- Lab: Analyze Customer Reviews with SQL
- Lab: Analyze Customer Reviews with Python Notebooks
