Domina la IA. Transforma tu carrera.

Master the Claude API from your first request to production deployment

Boost your productivity with a personalized AI assistant

GDPR compliance, ethics, and best practices for deploying AI

AI Act, GDPR and AI governance training funded by OPCO for DPOs, CTOs and managers

Prompt techniques and patterns for professional-grade results

Build AI workflows with n8n, Make.com, and intelligent agents

Design and deploy autonomous AI agents to automate complex tasks

Build production-ready AI applications with open-source LangChain and LangGraph

Deploy Llama, Mistral, and CodeLlama in production with Ollama to reduce your API costs by 80-95%

Build production-ready voice conversational agents with real-time architecture, <2s latency optimization, and robust error handling

Master Ragas, TruLens, and production metrics to guarantee the quality of your RAG systems

Cut your AI API bills by 40–70% without sacrificing quality

Build autonomous AI systems with CrewAI: roles, tasks, crews, and production pipelines

Master Anthropic's Model Context Protocol by building 5 production-ready MCP servers: filesystem RAG, PostgreSQL queries, REST API wrappers, web scraping, and multi-tool orchestration

Build systematic evaluation frameworks for any LLM system: BLEU/ROUGE/BERTScore, prompt A/B testing, cost-latency trade-offs, and custom evaluation harnesses

Secure your LLM deployments against prompt injection, jailbreaks, and adversarial attacks — from threat modeling to production-grade guardrails

Bridge the gap between tutorials and production: chunking strategies, hybrid search, reranking, and domain-specific evaluation for support, legal, and medical RAG.

Choose the right agentic framework for your project. Compare LangGraph, CrewAI, and AutoGen through 3 decision trees and the same real-world task implemented in all three.

Build intelligent email workflows that read, classify, and respond automatically using n8n and Claude

Safe patterns for AI agents that mutate databases: transactions, idempotency, rollback strategies, audit logging, and four production case studies across PostgreSQL, MongoDB, and DynamoDB.

Three production case studies, a cost simulator sandbox, and a RAG-vs-fine-tuning ROI calculator. For engineers deciding which vector database to use and how to keep RAG costs under control at scale.

Master the architecture of stateful AI agents. From conversation history and vector-backed long-term memory to distributed state machines and multi-tenant isolation — with 5 production sandbox exercises using LangGraph, Redis, and Claude.

Master VRAM management, quantization trade-offs, and GPU failover for Ollama and vLLM in production. Includes 10 real troubleshooting scenarios with exact fixes.