Building and Deploying Generative AI Models

Transition from theoretical concepts to production-ready engineering in this hands-on course which is the final part in "Fundamentals of Generative AI" specialization. Designed for learners ready to move beyond the theory, this course focuses entirely on construction: you won't just learn about Large Language Models (LLMs); you will build, refine, and deploy them. We start at the foundational level, coding different types of Transformer architectures from scratch using PyTorch. Through high-performance training with Automatic Mixed Precision and ROUGE/BLEU evaluation, you will learn the techniques to scale custom components into optimized systems. By utilizing pre-trained models and weighing performance trade-offs, you will gain the insight needed to select the most efficient path for large-scale deployment. Moving to applied architecture, you will master Retrieval Augmented Generation (RAG) using LangChain, learning to evaluate pipelines and apply advanced techniques such as different chunking strategies, reranking and compression, and query transformation. You'll also navigate model selection as well as the critical trade-offs between RAG and Fine-tuning. Finally, you will step into the future of AI by developing autonomous Agents. You will bridge the gap between development and production by setting up a professional workflow with Poetry and deploying a Summarizer AI Agent directly to the Google Cloud Platform (Vertex AI). By the end of this course, you will possess a tangible portfolio of code and a live deployment, proving your ability to engineer robust Generative AI solutions

• Build and evaluate Transformer-based models using PyTorch and industry metrics
• Engineer Retrieval-Augmented Generation pipelines with LangChain
• Deploy autonomous AI agents on Google Cloud Platform

• Basic topic familiarity
• Readiness for applied exercises and case work