Summary of 1. Introduction to AI and Machine Learning on Google Cloud

1. Introduction to AI and Machine Learning on Google Cloud

An introduction to AI and Machine Learning (ML) on Google Cloud is a course designed to equip you with the knowledge and skills to leverage Google Cloud's AI and ML tools and services. It covers the entire lifecycle of bringing AI solutions to life, from understanding the fundamental concepts of AI and ML to building and deploying your own models.

Teacher: admin admin

Skill Level: Beginner

Announcements

1.1.1 Course Introduction

1.1.2 AI Foundation Introduction

1.1.3 Why AI

1.1.4 AI / ML Architecture on Google Cloud

1.1.5 Google Cloud Infrastructure

1.1.6 Data and AI Products

1.1.7 ML Model categories

1.1.8 Big Query ML

1.1.9 Lab Introduction

1.1.10 Summary

1.2.1 Introduction

1.2.2 AI Development options

1.2.3 Pre trained API's

1.2.4 Vertex AI

1.2.5 Auto ML

1.2.6 Custom Training

1.2.7 Lab Introduction

1.2.8 Summary

1.3.1 Introduction

1.3.2 ML Workflow

1.3.3 Data preperation

1.3.4 Model Development

1.3.5 Model Serving

1.3.6 ML Ops and Workflow Automation

1.3.7 Lab Introduction

1.3.8 How a Machine Learns

1.3.9 Summary

1.4.1 Introduction

1.4.2 Generative AI and Workflow

1.4.3 Gemini Multimodel

1.4.4 Prompt design

1.4.5 Model Tuning

1.4.6 Model Garden

1.4.7 AI Solution

1.4.8 Lab Introduction

Course info

1. Introduction to AI and Machine Learning on Google Cloud

General

Announcements

1.1 AI FOUNDATION

1.1.1 Course Introduction

1.1.2 AI Foundation Introduction

1.1.3 Why AI

1.1.4 AI / ML Architecture on Google Cloud

1.1.5 Google Cloud Infrastructure

1.1.6 Data and AI Products

1.1.7 ML Model categories

1.1.8 Big Query ML

1.1.9 Lab Introduction

1.1.10 Summary

1.2 AI DEVELOPMENT OPTIONS

1.2.1 Introduction

1.2.2 AI Development options

1.2.3 Pre trained API's

1.2.4 Vertex AI

1.2.5 Auto ML

1.2.6 Custom Training

1.2.7 Lab Introduction

1.2.8 Summary

1.3 AI DEVELOPMENT WORK FLOW

1.3.1 Introduction

1.3.2 ML Workflow

1.3.3 Data preperation

1.3.4 Model Development

1.3.5 Model Serving

1.3.6 ML Ops and Workflow Automation

1.3.7 Lab Introduction

1.3.8 How a Machine Learns

1.3.9 Summary

1.4 GENERATIVE AI

1.4.1 Introduction

1.4.2 Generative AI and Workflow

1.4.3 Gemini Multimodel

1.4.4 Prompt design

1.4.5 Model Tuning

1.4.6 Model Garden

1.4.7 AI Solution

1.4.8 Lab Introduction

1. Introduction to AI and Machine Learning on Google Cloud