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Introduction to AI and Machine Learning on Google Cloud

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions.

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project based on the different goals of users, including data scientists, AI developers, and ML engineers.

GK# 899023 Vendor# GCP-INTR0-AI-ML
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Who Should Attend?

Professional AI developers, data scientists, and ML engineers who want to build ML models, develop AI or ML applications or solutions and build end-to-end ML pipelines on Google Cloud

What You'll Learn

Skills Gained

  • Recognize the data-to-AI technologies and tools offered by Google Cloud.
  • Use generative AI capabilities in applications.
  • Choose between different options to develop an AI project on Google Cloud.
  • Build ML models end-to-end by using Vertex AI.

Course Outline

Module 1: AI Foundations

  • Why Google?
  • AI/ML framework on Google Cloud
  • Google Cloud infrastructure
  • Data and AI products
  • ML model categories
  • BigQuery ML
  • Lab introduction: BigQuery ML

Module 2:AI Development Options

  • AI development options
  • Pre-trained APIs
  • Vertex AI
  • AutoML
  • Custom training
  • Lab introduction: Natural Language API

Module 3: AI Development Workflow

  • How a machine learns
  • ML workflow
  • Data preparation
  • Model development
  • Model serving
  • MLOps and workflow automation
  • Lab introduction: AutoML

Module 4: Generative AI

  • Generative AI and LLM
  • Generative AI use case: Duet AI
  • Model Garden
  • Generative AI Studio
  • AI solutions
  • Lab introduction: Generative AI Studio

Prerequisites

Having one or more of the following:

  • Basic knowledge of machine learning concepts
  • Prior experience with programming languages such as SQL and Python