Introduction to AI and Machine Learning on Google Cloud
- Course Code GO9095
- Duration 1 day
Course Delivery
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Course Delivery
This course is available in the following formats:
-
Company Event
Event at company
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Virtual Learning
Learning that is virtual
Request this course in a different delivery format.
Course Overview
TopThis 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.
Products:
- Gemini multimodal
- Gemini Enterprise
- NotebookLM
- BigQuery ML
- Vertex AI, Vertex AI Studio, Vertex AI Agent Builder, Vertex AI Pipelines
- Natural Language API
- AutoML
Updated July 2026
Virtual Learning
This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.
Course Schedule
TopTarget Audience
TopCourse Objectives
TopAfter this course participants should be able to:
- 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 Content
TopModule 0: Course Introduction
- Define the course objectives.
- Recognize the course structure
Module 1: AI Foundations
- Recognize the AI/ML framework on Google Cloud.
- Identify the major components of AI infrastructure.
- Define the data and ML products on Google Cloud and how they support the data-to-AI lifecycle.
- Build an ML model with BigQuery ML to bring data to AI.
- Lab: Predict Visitor Purchases with BigQuery ML
Module 2: Generative AI
- Define generative AI and foundation models.
- Recognize the prompt-to-production lifecycle and its associated tools.
- Define AI agents and their core components.
- Identify Google Cloud tools and technologies for building AI agents.
- Lab: Get started with Vertex AI Studio
Module 3: AI Development Options
- Define different options to build an ML model with Vertex AI on Google Cloud.
- Identify the features and use cases of pre-trained APIs, AutoML, and custom training.
- Use the Natural Language API to analyze text.
- Lab: Entity and Sentiment Analysis with Natural Language API
Module 4: AI Development Workflow
- 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 with Vertex AI.
- Lab: Vertex AI: Predict Loan Risk with AutoML
Module 5: Course Summary
- Recognize the primary concepts, tools, technologies, and products learned in the course.
Course Prerequisites
TopHaving one or more of the following:
- Basic knowledge of machine learning concepts
- Prior experience with programming languages such as SQL and Python
Test Certification
TopNone
Follow on Courses
TopNo recommendation
Further Information
Top- Official course book provided to participants
