Miguel Otero Pedrido is the founder of The Neural Maze, a hub for machine learning (ML) projects where concepts are explained step-by-step with code, articles, and video tutorials. He is a seasoned AI professional with extensive experience in developing and implementing AI solutions across various industries. Miguel has a strong background in machine learning, natural language processing, and computer vision, and has contributed to numerous projects that leverage AI to solve complex problems. Passionate about sharing his knowledge, he has mentored and taught, helping others understand and apply AI technologies effectively.
In this workshop, we’ll build a fully functional multimodal Telegram agent, putting into practice a wide range of concepts from the world of Agentic AI. This isn’t just another PoC — it's designed for those who are ready to level up and build complex, production-ready agentic applications.
Throughout the session, you’ll learn how to build a Telegram agent you can chat with directly from your phone, master the creation and management of workflows with LangGraph, and set up a long-term memory system using Qdrant as a vector database.
We’ll also leverage the fast Groq LLMs to power the agent’s responses, implement Speech-to-Text capabilities with Whisper, and integrate Text-to-Speech using ElevenLabs.
Beyond language, you’ll learn to generate high-quality images using diffusion models, and process visual inputs with Vision-Language Models such as Llama 3.2 Vision.
Finally, we’ll bring it all together by connecting the complete agentic application directly to Telegram, enabling a rich, multimodal user experience.
Read MoreIn this hands-on session, we'll move beyond demos and PoCs to dive into how to build complex agentic systems that work in real-world scenarios. We’ll start by covering the fundamentals of agents (short-term memory, long-term memory, tool use, reasoning techniques, etc), then introduce Agentic RAG and how it differs from traditional RAG, and show how to bring these concepts into production using LLMOps practices like agent monitoring, prompt versioning, dataset management and RAG evaluation. We'll wrap up with a real-time simulation of agents operating inside a video game, seeing all these concepts come to life in action.
Read MoreManaging and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance
Read MoreManaging and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance
Read More