Introduction to AI and Machine Learning on Google Cloud
Beginner level
recommended experience
Live Instructor training
live Q&A with chat
Official Google Content
latest content from Google
Hands-on labs
Real world practical experience
Course overview
This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing
AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
In this course you will
Recognize the data-to-AI technologies and tools provided by Google Cloud.
Build generative AI projects by using Gemini multimodal, efficient prompts, and model tuning.
Explore various options for developing an AI project on Google Cloud.
Create an ML model from end-to-end by using Vertex AI.
Course topics
Module 01: AI Foundations
Topics
🔸Why AI?
🔸AI/ML framework on Google Cloud
🔸Google Cloud infrastructure
🔸Data and AI products
🔸ML model categories
🔸BigQuery ML
🔸Lab introduction: BigQuery ML
Objectives
🔸Recognize the AI/ML framework on Google Cloud.
🔸Identify the major components of Google Cloud infrastructure.
🔸Define the data and ML products on Google Cloud and how they support the data-to-AI lifecycle.
🔸Build an ML model with BigQueryML to bring data to AI.
Activities
🔸Lab: Predicting Visitor Purchases with BigQuery ML
🔸Quiz
🔸Reading
Module 02: AI Development Options
Topics
🔸AI development options
🔸Pre-trained APIs
🔸Vertex AI
🔸AutoML
🔸Custom training
🔸Lab introduction: Natural Language API
Objectivies
🔸Define different options to build an ML model on Google Cloud.
🔸Recognize the primary features and applicable situations of pre-trained APIs, AutoML, and custom training.
🔸Use the Natural Language API to analyze text.
Actitivies
🔸Lab: Entity and Sentiment Analysis with Natural Language API
🔸Quiz
🔸Reading
Module 03: AI Development Workflow
Topics
🔸ML workflow
🔸Data preparation
🔸Model development
🔸Model serving
🔸MLOps and workflow automation
🔸Lab introduction: AutoML
🔸How a machine learns
Objectives
🔸Define the workflow of building an ML model.
🔸Describe MLOps and workflow automation on Google Cloud.
🔸Build an ML model from end to end by using AutoML on Vertex AI.
Activities
🔸Lab: Vertex AI: Predicting Loan Risk with AutoML
🔸Quiz
🔸Reading
Module 04: Generative AI
Topics
🔸Generative AI and workflow
🔸Gemini multimodal
🔸Prompt design
🔸Model tuning
🔸Model Garden
🔸AI solutions
🔸Lab introduction: Vertex AI Studio
Objectives
🔸Define generative AI and foundation models.
🔸Use Gemini multimodal with Vertex AI Studio.
🔸Design efficient prompt and tune models with different methods.
🔸Recognize the AI solutions and the embedded Gen AI features.
Activities
🔸Lab: Getting Started with Vertex AI Studio
🔸Quiz
🔸Reading
Class dates
Introduction to AI and Machine Learning on Google Cloud - schedule
June 24, 2025
September 16, 2025
December 16, 2025
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