Introduction to AI and Machine Learning on Google Cloud (AIMLGC)

 

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.

Who should attend

Professional AI developers, data scientists, and ML engineers who want to build predictive and generative AI projects on Google Cloud.

Prerequisites

Having one or more of the following:

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

Course Objectives

  • 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.

Outline: Introduction to AI and Machine Learning on Google Cloud (AIMLGC)

Module 1 - 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 2 - AI Development Options

Topics:

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

Objectives:

  • 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.

Activities:

  • Lab: Entity and Sentiment Analysis with Natural Language API
  • Quiz
  • Reading

Module 3 - 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 4 - 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

Module 5 - Course Summary

Topics:

  • Course Summary

Objectives:

  • Recognize the primary concepts, tools, technologies, and products learned in the course.

Prices & Delivery methods

Online Training

Duration
1 day

Price
  • US$ 595
Classroom Training

Duration
1 day

Price
  • on request

Click on town name or "Online Training" to book Schedule

This is an Instructor-Led Classroom course
Instructor-led Online Training:   This is an Instructor-Led Online (ILO) course. These sessions are conducted via WebEx in a VoIP environment and require an Internet Connection and headset with microphone connected to your computer or laptop.
This is a FLEX course, which is delivered simultaneously in two modalities. Choose to attend the Instructor-Led Online (ILO) virtual session or Instructor-Led Classroom (ILT) session.

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Italy

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Slovenia

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