Integrate a generative AI personal assistant into your applications without being an expertenes

In this session, you will learn how to easily create and integrate AI-generated personal assistants into your Python applications, without the need for previous experience, and how to integrate it into any application using APIs call.

It is aimed at anyone with minimal knowledge of Python and the cloud.

Building Generative AI Applications with Pythonenes

In this practical workshop you will learn concepts and functionalities of generative AI, through step-by-step exercises in Python using Amazon Bedrock.

Create your own personal generative AI assistant on WhatsApp... with multilingual skills.enes

Learn how to develop an application that integrates a generative AI assistant in WhatsApp. You will be able to chat and send voice notes in multiple languages, receiving transcriptions in real time

Add Generative AI to a JavaScript Web Appenes

Learn how to integrate generative AI into react JS applications with minimal code changes, using authentication to invoke a generative AI API.

The Art of Prompt Engineering: Unlocking the Full Potential of Generative AIenes

The rise of generative AI has made prompt engineering an essential skill. Through practical examples I'll demonstrate how to craft effective prompts that maximize AI models' potential, learn proven techniques, and understand how small changes in prompts can significantly impact AI responses. Whether you're new to AI or an experienced practitioner, you'll discover the strategies that make the difference between basic and exceptional AI interactions.

Building Vector Search Solutions for Text, Images, and Video Contentenes

Data exploration has moved beyond text; now data encompasses images and videos. In today's data-driven world, processing and analyzing large volumes of data efficiently is crucial for many applications. Let's explore together the concept of RAG and how to create and manage text and image embeddings for similarity searches in a PostgreSQL database. We'll dive into a practical example using Python to show you how to create vector bases of images and text for smarter searches. Additionally, we'll explore how to search and understand videos

Let's explore together the concept of RAG and how to create and manage text and image embeddings for similarity searches in a PostgreSQL database. We'll dive into a practical example using Python to show you how to create vector bases of images and text for smarter searches. Additionally, we'll explore how to search and understand videos.

Building Multi-Agent Applications: Divide and Conquerenes

As AI applications become more complex, single-agent systems often struggle with complex, multi-faceted tasks. This talk introduces a practical approach to building multi-agent systems where specialized agents work collaboratively under a coordinator agent's supervision.

Key Takeaways
- Understanding multi-agent architecture patterns
- Implementing agent coordination mechanisms
- Best practices for agent specialization
- Practical patterns for error handling and recovery
- Real-world implementation examples

Building a RAG System for Video Search and Analysisenes

This talk addresses the challenge of making video content searchable and analyzable using modern AI techniques. While text and image RAG systems are common, video presents unique challenges due to its multimodal nature combining visual frames and audio content.

Key Takeaways:
- Implement multimodal RAG architecture for video content
- Master vector embedding techniques for frames and audio
- Build efficient vector search capabilities using PostgreSQL
- Deploy production-ready video content analysis solutions

Building Production-Ready Generative AI Applications: From Concept to Deploymentenes

Learn to build, optimize, and deploy production-grade generative AI applications using AWS services. This hands-on workshop guides participants through the entire development lifecycle, from initial concept to production deployment, focusing on best practices, security considerations, and scalable architectures. Free test environment

Target Audience:
- Software developers with intermediate Python experience
- ML/AI practitioners interested in generative AI
- Solution architects planning AI implementations
- Technical leads evaluating gen AI solutions

Prerequisites:
- Basic Python programming experience
- Familiarity with AWS console
- Understanding of basic ML concepts
- Laptop with admin rights to install required tools