GCP100AIML
Introduction to AI and Machine Learning on Google Cloud
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.
What you will learn
- 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.
Prerequisites
- Basic knowledge of machine learning concepts
- Prior experience with programming languages such as SQL and Python
Target audience
- Professional AI developers, data scientists, and ML engineers who want to build predictive and generative AI projects on Google Cloud
Training Program
6 modules to master the fundamentals
Objectives
- Define the course goal.
- Recognize the course objectives.
Topics covered
- →Course introduction
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.
Topics covered
- →Why AI?
- →AI/ML framework on Google Cloud
- →Google Cloud infrastructure
- →Data and AI products
- →ML model categories
- →BigQuery ML
- →Lab introduction: BigQuery ML
Activities
Lab: Predicting Visitor Purchases with BigQuery ML
Quiz
Reading
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.
Topics covered
- →AI development options
- →Pre-trained APIs
- →Vertex AI
- →AutoML
- →Custom training
- →Lab introduction: Natural Language API
Activities
Lab: Entity and Sentiment Analysis with Natural Language API
Quiz
Reading
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.
Topics covered
- →ML workflow
- →Data preparation
- →Model development
- →Model serving
- →MLOps and workflow automation
- →Lab introduction: AutoML
- →How a machine learns
Activities
Lab: Vertex AI: Predicting Loan Risk with AutoML
Quiz
Reading
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.
Topics covered
- →Generative AI and workflow
- →Gemini multimodal
- →Prompt design
- →Model tuning
- →Model Garden
- →AI solutions
- →Lab introduction: Vertex AI Studio
Activities
Lab: Getting Started with Vertex AI Studio
Quiz
Reading
Objectives
- Recognize the primary concepts, tools, technologies, and products learned in the course.
Topics covered
- →Course summary
Activities
Reading
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
Teaching Methods Used
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
February 20, 2026
Distanciel • Français
May 21, 2026
Distanciel • Français
August 20, 2026
Distanciel • Français
November 26, 2026
Distanciel • Français
January 2, 2026
Distanciel • Français
March 27, 2026
Distanciel • Français
April 24, 2026
Distanciel • Français
June 25, 2026
Distanciel • Français
July 24, 2026
Distanciel • Français
September 30, 2026
Distanciel • Français
October 28, 2026
Distanciel • Français
December 21, 2026
Distanciel • Français
March 27, 2026
Distanciel • Français
April 24, 2026
Distanciel • Français
June 25, 2026
Distanciel • Français
July 24, 2026
Distanciel • Français
September 30, 2026
Distanciel • Français
October 28, 2026
Distanciel • Français
December 21, 2026
Distanciel • Français
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote