Generative AI Engineer Program

Course Description

Trains participants in the advanced skills needed to design, develop, and deploy generative artificial intelligence models, covering techniques such as Generative Adversarial Networks (GANs), language models (e.g., GPT), and applying these technologies across domains like content creation, image generation, and data synthesis.

Learning Objectives

By the end of this course, participants will be able to:

• Understand the foundations of generative AI: gain in-depth knowledge of the core principles behind generative models such as GANs (Generative Adversarial Networks) and autoregressive models.
• Master advanced tools and frameworks: learn to use technologies like TensorFlow, PyTorch, and generative model APIs to build and deploy high-performing solutions.
• Develop practical applications: design concrete use cases such as image, text, or audio generation to address industrial or creative needs.
• Optimize and deploy generative AI solutions: integrate models into production environments considering scalability, performance, and cost constraints.
• Apply ethics and safety in AI solutions: identify and mitigate risks associated with generative models, such as bias, misuse, or societal impacts.

Who is this course for?

This course is intended for:

• Software developers: eager to master tools and frameworks related to generative AI to build innovative applications.
• Data scientists: looking to deepen their expertise in deep learning and advanced generative models.
• Technical architects: seeking to integrate generative AI into enterprise solutions and design suitable architectures.
• AI students and researchers: wanting to explore the latest innovations in generative models for academic or industrial projects.
• IT professionals in career transition: motivated to leverage generative AI to transform creative processes or automate complex tasks.

Prerequisites

No specific prerequisites are required.

Course Syllabus

Day 1: Introduction to Generative AI

• Objective: Understand the theoretical foundations and key concepts.
• Content:
  Core concepts of generative AI.
  Overview of generative models: GANs, autoencoders, diffusion models, Transformers.
  Practical applications and use cases.

Day 2: Fundamentals of generative models

• Objective: Explore mathematical foundations and key algorithms.
• Content:
  Introduction to probabilistic distributions and synthetic data generation.
  Supervised vs. unsupervised learning algorithms.
  Case study: tabular data generation.

Day 3: Exploring GANs (Generative Adversarial Networks)

• Objective: Master the operation and implementation of GANs.
• Content:
  GAN architecture: generator and discriminator.
  Challenges and optimization (instability, mode collapse).
  Hands-on: implement a GAN for image generation.

Day 4: Autoregressive models and variational autoencoders (VAE)

• Objective: Understand and apply these models for content generation.
• Content:
  Autoencoders: structure and applications (compression, generation).
  Autoregressive models (GPT, LSTM): sequential text generation.
  Hands-on: generate creative text with GPT.

Day 5: Deep dive into Transformers and Deep Learning

• Objective: Explore the basics of Transformers and their role in generative AI.
• Content:
  Transformer principles and attention mechanism.
  Exploration of large models (GPT-3, BERT).
  Workshop: build a Transformer-based generative model.

Day 6: Generative AI for computer vision

• Objective: Apply generative models to images.
• Content:
  Introduction to diffusion models (Stable Diffusion, DALL·E).
  Image generation and manipulation.
  Workshop: generate stylized or photorealistic images.

Day 7: Generative AI for audio and video

• Objective: Explore models for multimedia generation.
• Content:
  Models for music, audio, and video generation.
  Introduction to WaveNet and deepfakes.
  Workshop: speech synthesis with AI tools.

Day 8: Deploying and optimizing generative AI solutions

• Objective: Integrate generative solutions into real-world environments.
• Content:
  Model optimization for production.
  Cloud deployment (Azure, AWS, GCP).
  Workshop: deploy a generative model via a REST API.

Day 9: Ethics and safety in generative AI

• Objective: Identify risks and adopt responsible practices.
• Content:
  Ethical issues: bias, misinformation, copyright.
  Security and regulation of generative models.
  Case study: critical analysis of a generative AI project.

Day 10: Capstone project

• Objective: Apply acquired skills in a complete project.
• Content:
  Project definition (choose a domain: text, image, video).
  Group or individual implementation.
  Presentation and retrospective on developed solutions.

Program Highlights

• Comprehensive, progressive curriculum: a well-structured path from fundamentals to advanced applications for deep understanding.
• Practical, contextual approach: numerous hands-on workshops to work with generative AI tools and models in real contexts.
• Best-in-class tooling: use of the most recent and relevant frameworks and platforms for generative AI (Transformers, GANs, cloud platforms).
• Real-world capstone: a full day devoted to the final project, integrating learning in a professional scenario.
• Ethics and safety focus: deep reflection on ethical issues, bias, and regulation to ensure responsible use.
• Market-aligned: designed to meet current business needs for innovative, high-performing AI solutions.
• Support and guidance: expert mentoring and resources to ensure lasting upskilling.

Teaching Methods and Tools Used

• Live demonstrations with data science services.
• Hands-on workshops and real case studies across various sectors (industry, retail, healthcare).
• Feedback: sharing best practices and common pitfalls in companies.
• Simulations and tools: using simulators for interactive workshops.

Assessment

• End-of-course multiple-choice quiz to test understanding of covered concepts.
• Practical case studies or group discussions to put acquired knowledge into practice.
• Ongoing evaluation during practical sessions.
• Hands-on project: complete build at the end of modules to consolidate learning.

Normative References

• Well-Architected Cloud Framework.
• GDPR (General Data Protection Regulation).
• ISO 27001, SOC 2 (Service Organization Control).
• NIST Cybersecurity Framework.