Introduction to AI and Machine Learning on Google Cloud (GC-IAIMLGC)

This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.


Who should attend?

AI developers, data scientists, and ML engineers


Objectives

  • Recognize the data-to-AI technologies and tools offered  by Google Cloud.
  • Build generative AI projects by using Gemini multimodal, efficient prompts, and AI agent builders. 
  • Choose between different Google Cloud product options to develop an AI project.
  • Build ML models end to end by using Vertex AI.
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Detailed Course Outline

Module 0: Course Introduction

  • Course introduction


Module 01: AI Foundations

  • A use case
  • AI on Google Cloud
  • AI infrastructure 
  • AI models
  • BigQuery ML
  • Hands-on lab: Predict Visitor Purchases with BigQuery ML


Module 02: Generative AI

  • Generative AI on Google Cloud
  • Foundation models
  • Idea to app
  • Prompt engineering
  • Deployment and model tuning 
  • AI agents
  • Agent building with Google Cloud
  • Hands-on lab: Get started with Vertex AI Studio


Module 03: AI Development Options

  • AI development options
  • Vertex AI
  • AutoML
  • Pre-trained APIs
  • Custom training 
  • Hands-on lab: Entity and Sentiment Analysis with Natural Language API


Module 04: AI Development Workflow

  • ML workflow 
  • Data preparation
  • Model development 
  • Model serving
  • MLOps and workflow automation
  • How a machine learns (optional)
  • Hands-on lab: Vertex AI: Predict Loan Risk with AutoML


Module 05: Course Summary

  • Course summary
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