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Training goals

code: G-GBQDP | version: 1.0

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.

 

What you'll learn

  • 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.

 

Audience

This course is intended for: data analysts, data engineers, and other data professionals who wish to use Gemini in BigQuery to boost productivity and understand their unstructured data.

 

Products

  • Gemini
  • BigQuery
  • Vertex AI

Conspect Show list

  • Gemini on BigQuery
    • Topics
      • Gemini on Google Cloud
      • Overview of Gemini on BigQuery
      • Introduction to course use case
    • Objectives
      • Understand capabilities of Gemini on Google Cloud.
      • Understand capabilities of Gemini on BigQuery.
  • Data Exploration and Preparation
    • Topics
      • Data exploration and preparation
      • Insights
      • Table Explorer
    • Objectives
      • Discover tools that support data exploration.
      • Identify the benefits and restrictions of Insights and Table Explorer.
      • Explore data cleaning and pipeline development features in BigQuery.
    • Activities
      • Lab: Explore Data with Gemini in BigQuery
  • Code Development with Gemini
    • Topics
      • Gemini for writing code
      • Troubleshooting and testing with Gemini
      • Prompting best practices
    • Objectives
      • Explore using Gemini for writing code.
      • Identify how Gemini can assist with troubleshooting.
      • Discover prompting best practices.
    • Activities
      • Lab: Develop Code with Gemini in BigQuery
  • Data Canvas
    • Topics
      • Introduction to Data Canvas
      • Data Canvas capabilities
      • Prompting best practices for Data Canvas
    • Objectives
      • Explore Data Canvas features.
      • Discover prompting best practices for Data Canvas.
    • Activities
      • Lab: Use Data Canvas to Visualize and Design Queries
  • Working with Gemini Models in BigQuery
    • Topics
      • BigQuery ML
      • Using Gemini in your SQL queries
      • Gemini in BigQuery Notebooks
    • Objectives
      • Discover the capabilities of BigQuery ML.
      • Explore using Gemini in your SQL queries.
      • Explore using Gemini in Jupyter Notebooks.
    • Activities
      • Lab: Analyze Customer Reviews with SQL
      • Lab: Analyze Customer Reviews with Python Notebooks
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Additional information

Prerequisites
  • Prior experience with programming languages including SQL and/or Python.
  • Basic knowledge of ML and generative AI.
Difficulty level
Duration 1 day
Certificate

The participants will obtain certificates signed by Google Cloud (course completion).
Trainer

Authorized Google Cloud Trainer

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