Google Skills: the advanced generative AI learning path for developers

Who is this learning path for?

The learning path "Advanced Generative AI for Developers" is hosted on Google Skills and is aimed at three profiles:

• Application developers who want to integrate generative AI into their products
• ML engineers / Data scientists who want to deepen their understanding of generative architectures
• Technically curious learners who have already completed the introductory generative AI path (recommended prerequisite)

The format: 12 online modules, for a total duration of about 3 hours. Each module is independent, but the suggested order follows a logical progression - from architectural foundations to practical and ethical aspects.

The foundations: how generative AI works

The first four modules lay the theoretical groundwork. Even if you already use LLMs daily, understanding how they work changes the way you use them.

Module 1 - Introduction to image generation

This module covers diffusion models, which originate from thermodynamics. The principle: start from random noise and progressively "denoise" it to generate a coherent image. This is the technology behind Imagen, DALL-E, and Stable Diffusion. The module shows how these models are integrated into Google Cloud tools.

Module 2 - The attention mechanism

Attention is the fundamental concept that made modern LLMs possible. This module explains how a neural network can focus on specific parts of a sequence rather than processing everything uniformly. Concrete applications: machine translation, text summarization, question answering.

Module 3 - Encoder/Decoder architecture

The encoder-decoder duo is the foundation of sequence-to-sequence tasks: translation, summarization, code generation. The encoder compresses the input into an internal representation, the decoder turns it into output. This is the architecture that preceded Transformers and that you need to understand to appreciate what has changed.

Module 4 - Transformers and BERT

The central module. The Transformer architecture (2017, Google) revolutionized NLP by replacing recurrence with self-attention, which allows entire sequences to be processed in parallel. The module also covers BERT (Bidirectional Encoder Representations from Transformers), which reads text in both directions simultaneously. Applications: text classification, entity extraction, sentiment analysis.

In practice: Vertex AI and advanced techniques

After the theory, four modules focused on hands-on practice with Google Cloud.

Module 5 - Image captioning models

How to automatically generate textual descriptions from images. This module teaches deep learning approaches for building captioning systems - useful for accessibility, image search, and visual content indexing.

Module 6 - Introduction to Vertex AI Studio

Hands-on module on Vertex AI Studio, Google Cloud's interface for prototyping and deploying generative AI applications. You'll learn to use Gemini (Google's multimodal model) to generate text, analyze images, and build interactive applications - all without managing infrastructure.

Module 7 - Vector search and embeddings

This module covers semantic search : how to turn text into numerical vectors (embeddings) to find results by meaning rather than exact keywords. It covers hybrid approaches and, above all, RAG (Retrieval-Augmented Generation) - the technique that lets an LLM rely on external documents to answer with precision.

Module 8 - Inspecting multimodal documents with Gemini

How to use Gemini to extract information from documents that combine text and images. Multimodal queries, invoice analysis, understanding technical diagrams - a glimpse of what multimodal makes possible in the enterprise.

Responsible AI: 3 ethics modules

Google devotes a quarter of the path to responsible AI. This isn't a minor detail - it's an integral part of the training.

Module 9 - Fairness and bias

How to identify bias in AI models and put fair practices in place. Bias doesn't come only from data - it also comes from design choices, evaluation metrics, and favored use cases.

Module 10 - Interpretability and transparency

A model that gives the right answer but can't be explained is a problem in many contexts (medical, legal, financial). This module covers modelexplainability methods and transparency best practices.

Module 11 - Privacy and security

Protecting training data, securing deployed models, risks of personal data extraction. An essential module for anyone deploying generative AI in production.

MLOps: deploying and maintaining

Module 12 - MLOps for generative AI

The final module covers production deployment of generative models. Specific challenges: model version management, output monitoring, scaling, inference costs. The module presents Vertex AI solutions for automating these operations.

This is where theory meets reality: a model that works in a notebook is not a model in production.

Why take this learning path?

In 3 hours, this path covers the full spectrum of generative AI - from the internal architecture of models to deployment and ethics. A few reasons to take it:

• Free - hosted on Google Skills, accessible to everyone
• Progressive structure - from foundations to practice, without assuming prior ML knowledge
• Vendor-specific but transferable - the concepts (Transformers, attention, RAG, MLOps) are universal, even though the tools presented are Google Cloud
• Responsible AI built in - 3 modules out of 12 dedicated to ethics, which is rare in technical training
• Available in French - the path is localized into 10 languages including French

For developers who already use LLMs via APIs (OpenAI, Anthropic, Google), understanding the internal mechanisms - attention, embeddings, diffusion - makes it easier to prompt better, debug better, and make more informed architectural choices.

Overview of the 12 modules