🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
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.

Google Cloud
✓ Official training Google CloudLevel Fundamentals⏱️ 1 day (7h)

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 Agent Platform.
  • Identify the core infrastructure components of Google Cloud that support AI and ML.
  • Build and evaluate machine learning models directly within BigQuery using BigQuery ML.
  • Compare pre-trained APIs, AutoML, and custom training options on Google Cloud to select the appropriate model building method.
  • Use pre-trained APIs, such as the Natural Language API, to perform text analysis.
  • Explain MLOps concepts and tools for automating machine learning workflows 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

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
  • →Agent Platform
  • →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 Agent Platform.
Topics covered
  • →ML workflow
  • →Data preparation
  • →Model development
  • →Model serving
  • →MLOps and workflow automation
  • →Lab introduction: AutoML
  • →How a machine learns
Activities

Lab: Agent Platform: Predicting Loan Risk with AutoML

Quiz

Reading

Objectives
  • Define generative AI and foundation models.
  • Use Gemini multimodal with Agent 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: Agent Studio
Activities

Lab: Getting Started with Agent Studio

Quiz

Reading

Objectives
  • Recognize the primary concepts, tools, technologies, and products learned in the course.
Topics covered
  • →Course summary
Activities

Reading

Related Trainings

Google Cloud

Application Development with LLMs on Google Cloud

In this course, you'll dive into the details of using Large Language Models (LLMs) in your applications. You'll start by exploring the core principles that underpin prompting LLMs. Next, you will focus on Google's latest family of models, Gemini. You'll explore the various Gemini models and their multimodal capabilities. This includes a deep dive into effective prompt design and engineering within the Agent Studio environment. Then, the course moves to application development frameworks and how to implement these concepts into your applications.

1 d
Intermediate
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Upcoming sessions

September 30, 2026
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October 28, 2026
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November 26, 2026
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December 21, 2026
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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.

790€ excl. VAT

per learner

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