Python shines in RAG (Retrieval-Augmented Generation) systems due to its efficiency in orchestrating various processes and its extensive libraries, such as LangChain and Hugging Face Transformers. The building blocks for RAG include data extraction and preprocessing, transforming data into vectors via embedding models, and using vector databases for retrieval. Python excels in setting up data pipelines for indexing, retrieval, and generation, integrating different components, and ensuring low-latency, high-efficiency real-time processing. Real-world applications of RAG systems showcase Python's benefits and challenges in implementation, demonstrating its versatility and robustness in managing complex data flows and interactions.

Discover how to build powerful real-time IoT systems using an open-source stack. This presentation will cover how to store, process high volume sensor data using Raspberry Pi, Python InfluxDB 3 and then using Grafana to create a dynamic real time dashboard. You will learn how fun and easy it is to process time series data for your needs including for Edge ML.

Foundations of LLMs and Python Basics; Understanding Natural Language Processing; Transformers and Attention; LLM Development: Fine-tuning and Prompt Engineering; Retrieval-Augmented Generation (RAG); Introduction to LLM Agents; Advanced Topics for Production LLM Application

Learn how to use Python with Pulumi to provision AWS resources, bringing modern software development practices to your infrastructure management.

Enhance your analytical skills with our two-week workshop on A/B Testing. Perfect for students, data analysts, and professionals, this workshop covers essential concepts and practical applications of A/B testing to help you make data-driven decisions. Basic knowledge of statistics and Python is recommended.