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.

✓ Formation officielle Google CloudNiveau Fundamentals⏱️ 1 jour (7h)

Ce que vous allez apprendre

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.

Prérequis

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

Public cible

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

Programme de la Formation

6 modules pour maîtriser les fondamentaux

Objectifs

Define the course goal.
Recognize the course objectives.

Sujets abordés

→Course introduction

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.