Design and Implementation
of a Generative AI–Based Solution

Course Description

This course enables learners to acquire the skills needed to design, deploy, and operate a solution based on generative AI.

Who is this course for?

The course is intended for a broad audience, including:
Project managers and technical leads seeking to integrate generative AI solutions into business processes.
Data scientists and AI engineers wishing to deepen their expertise in generative solutions.
R&D managers interested in applying AI in innovative projects.
Consultants and trainers supporting companies in digital transformation through AI.
Entrepreneurs looking to leverage generative AI capabilities to optimize their processes.

Prerequisites

Basic knowledge of information systems.

Learning Objectives

By the end of this course, participants will be able to:
Understand the foundations of generative AI: grasp the principles and technologies underlying generative AI.
Design a generative AI solution tailored to specific business needs.
Implement AI models using appropriate tools and platforms.
Integrate AI into existing business processes.
Ensure governance and ethics for generative AI solutions.

Curriculum

The course is structured around several key modules:
Introduction to Generative AI and Solution Design

• Generative AI fundamentals (neural networks, GANs, VAEs, etc.).
• Designing a generative AI solution tailored to business needs.

Implementation and Deployment of an AI Solution

• Data preparation and AI model training.
• Deploying AI solutions in the enterprise (cloud environments, performance management, etc.).

Governance, Ethics, and Deployment Strategy

• Governance and ethics of generative AI (bias, transparency, regulation).
• Long-term deployment strategy and maintenance of AI solutions.

Course Highlights

Balance of theory and practice for better mastery of concepts.
Specialized instructors with real-world experience in AI.
Access to online resources, live demos, and real case studies.
Open to all; no advanced technical prerequisites.

Teaching Methods and Tools

Live demonstrations.
Real case studies with analysis of concrete solutions.
Group work and real-time collaboration.
Feedback: sharing best practices and common pitfalls.

Assessment

Assessment is conducted in several ways:
Multiple-choice quizzes (MCQs) to test understanding of the concepts covered.
Hands-on case studies to apply knowledge.
Ongoing assessment during practical sessions.

Reference Standards

ISO 25010 (Systems and software quality): Used to assess the quality of AI systems, particularly in terms of reliability, maintainability, and model efficiency.
ISO 27001 (Information security): Ensures the security and confidentiality of data used in AI-based systems.
Ethical Guidelines for Trustworthy AI (European Commission): Provides a framework for the responsible development and use of artificial intelligence.