Master in Generative AI Solutions Engineering and Development

For profiles less familiar with the Python programming language, this leveling prework will give you the foundations you need to start this master. You will learn the fundamentals of the language through dynamic, self-correcting practical material, with access to the IT Specialist Python (ITS-303) certification.

Introduction to programming Introduction to Python Data types Variables Basic input and output operations Basic operators Boolean values Conditional execution

Loops Bitwise operations Lists Functions Tuples and dictionaries Exceptions

 This topic presents the concept of generative artificial intelligence, its different types and use cases from a programmatic perspective. It lays the groundwork for the development environment by installing Python, VSCode and the necessary extensions, introducing Git and Postman, and analyzing the range of generative AI providers (both cloud-based and open source), along with hardware requirements and alternatives. 

Definition of generative AI and its main types Use cases and programmatic applications of generative AI Setting up the development environment: Python, VSCode and extensions

Introduction to Git and Postman Generative AI providers, both cloud-based and open source Hardware requirements for different uses

This topic explains the different types of machine learning models (supervised and unsupervised) and covers the fundamental concepts of training, testing and inference, as well as evaluation metrics. It introduces deep learning and neural networks, data requirements and the problem of overfitting. It also reviews the hardware requirements (GPUs) and configuration parameters essential to training and inference in deep learning.

Supervised vs. unsupervised models Training, testing and inference concepts Evaluation metrics and validation of results Introduction to deep learning and neural networks

Amount of data required, generalization and overfitting Hardware requirements, use of GPUs and training parameters

This topic reviews the evolution of Natural Language Processing (NLP) before generative AI and explains the different types of models (discriminative, generative, autoregressive, VAEs and GANs), as well as generative diffusion. It also offers a historical and current perspective on Language Models (LLMs) and their different types, reviewing the state of the technology and the latest advances.

Historical evolution of NLP and pre-generative AI techniques Types of models: discriminative, generative, autoregressive, VAEs, GANs Generative diffusion and its principles

History and evolution of language models (LLMs) Different LLM architectures and their current state

This topic goes deeper into the theory behind Large Language Models (LLMs), covering mechanisms such as self-attention, multi-head attention and tokenization. It analyzes the architectures and evolution of GPT models (OpenAI), Google's models and Anthropic's models, as well as open source models. Finally, it distinguishes between concepts such as prompting, contextualization, RAG, fine tuning and training from scratch, and describes how to customize queries and optimize costs and results.

Theoretical foundations of LLMs: self-attention, multi-head attention, tokenization Architecture and evolution of GPT models (OpenAI) Architecture and evolution of Google's models Architecture and evolution of Anthropic's models Open source models: types and ways to use them

Adaptation techniques: prompting, contextualization, RAG, fine tuning, training from scratch Parameter customization, costs, evaluation and token calculation Practical uses of LLMs: chatbots, code generation, text analysis

This topic covers building and training a generative AI model from scratch. To do so, you will use a cloud machine with sufficient power and a sample dataset. It walks through the setup process, the network architecture, hyperparameter selection and the different training phases. The goal is to provide hands-on experience in creating a generative AI model.

Selecting and configuring a cloud machine Setting up the development environment for training Dataset review and cleaning

Defining the model architecture Running the training Final validation and room for improvement

This topic introduces image generation models and presents use cases where they add value. It explains architectures such as GANs and diffusion models, illustrated with Stable Diffusion and others. It covers calls to popular image generation models from Python. It also reviews audio and video models, such as Whisper, and the customization of image models with specific datasets to achieve style adjustments and output control.

Image generation models and use cases GAN architecture: generator and discriminator, additional applications Diffusion models (Stable Diffusion) and the “denoising” process

Using image generation APIs from Python Audio and video models (Whisper and others) Customizing image models: fine tuning and style adjustments

This topic offers a mathematical introduction to vector spaces and vector operations, including different ways of calculating distances. It defines the concept of embedding and its relationship with semantic distances, as well as generating embeddings via APIs. It reviews tools for working with vector indexes and goes deeper into practical use cases of semantic search and integration with LLMs (RAG).

Vector spaces: basic concepts and distance calculation Embeddings: vector representation of text and images Generating embeddings (OpenAI, Cohere) and Python tools

Semantic search and building vector indexes Practical applications of embeddings and semantic search Integrating embeddings and LLMs: RAG, best practices and optimizers

This module goes deeper into querying and integrating APIs using tools such as Postman, and into designing your own APIs with Python (FastAPI, Flask) to build generative AI backends. You will work with protocols such as the Model Context Protocol (MCP) and get an introduction to cloud computing, covering cloud AI services as well as model deployment. It addresses MLOps principles for the life cycle of generative AI systems, including monitoring, maintenance, and CI/CD. Containers, microservices, and deployment and scaling strategies are also covered.

Using APIs with Postman and building your own APIs (FastAPI, Flask) Introduction to the cloud for generative AI MLOps: life cycle, updates, monitoring, and maintenance Model Context Protocol (MCP)

CI/CD, containers (Docker), microservices, and orchestration Metric calculation and monitoring in production Deployment strategies (canary, blue-green) and autoscaling Cost optimization and capacity reservation

 This module provides a general introduction to the cloud environment and its relationship with processing and validating generative AI solutions, comparing different providers and cloud compute options. It analyzes data storage and management strategies, including the use of vector stores, and explores examples of implementing pipelines and ETL processes through serverless functions. It also addresses key aspects such as security and authentication on your own endpoints, along with cost management and best practices. 

Introduction to general cloud use and cloud compute testing Cloud data storage and management (vectorstores) Implementing pipelines and ETLs in the cloud, using serverless services

Security and authentication (API keys, endpoint protection) Cloud cost management, optimization, and expense mitigation

This module introduces the libraries and frameworks most widely used in developing generative AI solutions, such as PyTorch Lightning and TensorFlow, highlighting their capabilities for distributed training. It examines the Hugging Face ecosystem (Transformers and Diffusers) for using and customizing open source models. You will also work with LangChain and Langraph to build complex LLM-based systems, incorporating techniques such as RAG and memory. Gradio and Streamlit are presented as tools for rapidly developing simple interfaces (frontends). Finally, it covers integrating APIs with pure frontends and the use of monitoring tools. 

Deep learning libraries: PyTorch Lightning, Tensorflow Using Hugging Face: transformers and diffusers LangChain: RAG, memory, and prompt orchestration

Building frontends with Gradio and Streamlit Connecting APIs to frontends and monitoring

 This module brings together the knowledge acquired in previous modules to develop complete applications and automated processes. It covers the entire path: from conceptualizing a frontend to consume a generative AI API, to integrating LLMs with enterprise data through semantic search and private chatbots. It also introduces the concept of multimodal applications, combining different models to achieve more effective results, and analyzes how to extend functionality through intelligent agents and assistants. Finally, it emphasizes the importance of running unit and integration tests on generative solutions. 

Conceptualizing frontends and connecting them to generative AI APIs Integrating LLMs with enterprise data: semantic search and chatbots Multimodal applications and coordinating multiple models

Using agents and assistants with ReAct prompting Testing and QA of generative applications

You will learn to automate software development using AI, optimizing code generation, validation, and refactoring. You will then address its integration into the development environment (IDE), leveraging intelligent assistants to speed up tasks, reduce errors, and improve your workflow in real time.

Automatic code generation with AI AI integration in the development environment (IDE) Intelligent, context-aware autocompletion Real-time error detection and correction

Assisted refactoring Integration with tools and APIs Improved productivity and workflow

Design an application from scratch alongside Claude Code: architecture, interface, backend, database, testing, and security. You will learn to turn requirements into working code, automate the development workflow, and deploy the product to production through a reproducible, fast pipeline ready to keep evolving.

Claude Code: code planning, generation, refactoring, and debugging. Git and GitHub: version control and professional project management. Frontend, backend, and database: end-to-end construction of the application.

Testing, security, and quality: automated validations, error review and resolution, guardrails, and security. CI/CD and deployment: automation with GitHub Actions and publishing to the cloud.

Throughout the master, you have learned to develop solutions that in many cases already offered a certain level of automation. However, this module focuses on programming advanced agents and automation processes capable of managing different types of workflows, platforms, tools, integrations, and more, resulting in automation systems that are far more personalized and sophisticated than those you can build with low-code or no-code tools.

Determining the tools and external sources to use with ReAct prompting. Current services that offer assistant creation and options for connecting to different tools and services. Developing assistants and agents for specific purposes.

Multi-agent systems. Agent orchestration. Guided development of a complete solution that implements agents.

You will learn to automate interaction with the computer using OpenClaw, allowing AI to replicate human behavior on the system. The AI will be able to move the cursor, type, navigate, and operate desktop and web applications, executing complete workflows autonomously to optimize repetitive tasks and processes.

Automation of human-computer interaction Cursor and keyboard control through AI Navigation in web and desktop applications MCP and connection of external tools

Autonomous execution of complete workflows Integration of agents for operational tasks Reduction of manual intervention in repetitive processes

Design an SRE agent capable of receiving alerts, investigating metrics and logs, identifying the root cause of an incident, generating a fix, validating the code, and deploying it in a controlled way. You will build a complete workflow with human supervision, operational memory, observability, canary deployments, and automatic rollback.

Hermes Agent and tool calling: planning, autonomous execution, and coordination of the resolution process. OpenTelemetry, Prometheus, and Grafana: anomaly detection and analysis of metrics, traces, and logs. GitHub and CI/CD: patch generation, automated testing, pull request, and human approval.

Docker and Kubernetes: canary deployment, validation in production, and rollback in case of errors. Memory and guardrails: incident history, permissions, action limits, and full auditing.

This module reflects on the ethics of generative AI and the biases that can arise, proposing filtering and transparency mechanisms to mitigate risks. It also covers the regulations and standards related to data processing, anonymization, and copyright protection, emphasizing the importance of legal compliance and sound governance of AI systems.

Identifying and mitigating bias in generative AI Content filtering, transparency, and accountability Data privacy and data processing regulations

Copyright, usage licenses, and legal considerations The importance of ethical governance and regulatory compliance

This module presents the keys to planning and managing generative AI projects, designing an MVP and defining the appropriate requirements and models. It also addresses the estimation of resources and costs, team size and specialization, and the phases needed to develop and roll out the solution to production.

Conceptualization and design of an MVP Defining requirements and selecting the right model Determining data and preparing the dataset

Resource estimation, team building, and planning Cost calculation and rollout to production

At the end of the master, we will introduce the main advances that took place in the months prior to this module related to the development of generative AI solutions, so that students finish with a fully up-to-date profile and understand the speed at which the generative AI field is evolving.

Claude Code: code planning, generation, refactoring, and debugging. Git and GitHub: version control and professional project management. Frontend, backend, and database: end-to-end construction of the application.

Testing, security, and quality: automated validations, review and resolution of errors, guardrails, and security. CI/CD and deployment: automation with GitHub Actions and publishing to the cloud.