Ryan Ahmed – Become an LLM & Agentic AI Engineer: 14-Day Bootcamp
Here’s What You Get: Learn how to build, deploy, and automate powerful AI agents to accelerate your business results. Get instant access to 14 days of intensive training, expert weekly coaching calls, and a thriving AI community. 24+ hours of video content 300+ video lectures 14-Day extensive training Access to an exclusive AI community Weekly live group coaching calls (1 year access) Access to code, slides, & data Practice Opportunities with Solutions WHAT YOU WILL LEARN In this bootcamp, you’ll: Understand the foundations of Large Language Models (LLMs) and Agentic AI, including how LLMs are trained, fine-tuned, and deployed. Explore and benchmark open-source LLMs such as LLaMA, DeepSeek, Qwen, Phi, and Gemma using Hugging Face and LM Studio. Apply a proven 5-step framework to select the right AI model for your business: maximizing cost-efficiency, minimizing latency, & accelerating time to market. Design Retrieval-Augmented Generation (RAG) pipelines using LangChain, OpenAI embeddings, & ChromaDB for efficient document retrieval & question answering. Master data validation & structured output generation using the Pydantic library, including BaseModel, type hints, & parsed output creation from OpenAI models. Learn how to fine-tune pre-trained open-source LLMs using parameter-efficient methods like LoRA and tools such as Hugging Face’s TRL and SFTTrainer. Apply key components in Hugging Face Transformers library such as pipeline(), AutoTokenizer(), and AutoModelForCausalLM(). Master advanced prompt engineering techniques such as zero-shot, few-shot, and chain-of-thought prompting. Develop and deploy agentic AI workflows using LangGraph, mastering concepts like states, edges, conditional logic, and multi-stage nodes. Build a data science agent team using CrewAI, creating specialized agents for workflow planning, data analysis, model building, and predictive analytics. Build an advanced AI tutor system using Model-Context-Protocol (MCP) and OpenAI Agents SDK, enabling dynamic tool interoperability. Create and deploy intelligent autonomous AI agents using cutting-edge frameworks like AutoGen, OpenAI Agents SDK, LangGraph, n8n, and MCP. Develop real-world applications using API access to OpenAI, Gemini, and Claude for text generation and vision tasks. Evaluate LLMs using leaderboards like Vellum and Chat Arena, and conduct blind tests to objectively assess AI model performance. Build an interactive, transparent AI-powered Q&A system with a Gradio interface that displays answers along with source citations for enhanced user trust. Build an AI-powered resume editor that analyzes gaps between a resume & job description, & automatically tailors resumes/cover letters for targeted applications. Master dataset preparation and model evaluation techniques, including calculating accuracy, precision, recall, and F1-score using scikit-learn. Gain practical experience working with open-source datasets/models on Hugging Face, & apply quantization techniques like bitsandbytes to optimize performance. Deploy multi-model AI agents using AutoGen, integrating LLMs from OpenAI, Gemini, & Claude, enabling agent collaboration & human-in-the-loop oversight. Design & build AI-powered booking agents using LangGraph, enabling automated search & recommendation of flights & hotels through integration with external APIs. Design and automate end-to-end Agentic AI workflows using n8n, integrating services like Gmail, Google Sheets, Google Calendar, and OpenAI