Building Production-Ready AI Apps with Llama, Hugging Face, and Python

Event Description

Unleash the Power of Llama: A Practical Workshop on Building AI Applications

Join us for an intensive, hands-on workshop where you'll learn the complete workflow for developing real-world Artificial Intelligence applications using the state-of-the-art Llama model, the Hugging Face ecosystem, and Python.

Large Language Models (LLMs) like Llama have revolutionized AI development, and the Hugging Face platform provides the essential tools to deploy them effectivel... Show morey. This session moves beyond theory, focusing on practical implementation and best practices for creating scalable, performant, and safe AI applications. Whether you're a data scientist, machine learning engineer, or Python developer, you'll gain the critical skills needed to integrate cutting-edge LLMs into your projects.

What You Will Learn (Key Takeaways)

Mastering the Hugging Face Ecosystem: Learn to navigate the Hugging Face Hub, and utilize the transformers library, PEFT (Parameter-Efficient Fine-Tuning), and Inference Endpoints for efficient model deployment.

Integrating Llama: Understand how to load and use various Llama model versions (e.g., Llama 3) for text generation, classification, and advanced reasoning tasks.

Building RAG Systems: Develop a robust Retrieval-Augmented Generation (RAG) application using frameworks like LangChain or LlamaIndex to ground Llama's responses with your private data.

Developing & Deploying the App: Build a functional, interactive demo using Python libraries like Gradio or Streamlit and prepare your application for deployment.

Performance and Optimization: Explore techniques like quantization, Grouped-Query Attention (GQA), and efficient inference to ensure your Llama-powered app runs fast and cost-effectively.

Who Should Attend

This workshop is ideal for:

Python Developers looking to pivot into AI/ML.

Data Scientists and ML Engineers seeking hands-on experience with Llama and the Hugging Face stack.

Tech Leads and Architects evaluating open-source LLMs for product integration.

Prerequisites

Attendees should have intermediate proficiency in Python and a basic understanding of Machine Learning/Deep Learning concepts. A laptop with internet access will be required for the hands-on coding exercises.