LLM Experimentation, Deployment, and Ethical AI Foundations

Deploying large language models into production requires more than just API calls; it demands rigorous experimentation, efficient serving, and a strong commitment to safety. This course guides you through the entire lifecycle of LLM development, from initial tuning to scalable deployment and ethical alignment.\n\nYou will transition from basic prompting to understanding how to systematically evaluate, optimize, and deploy models. Through clear, text-based lessons, you will learn how to set up robust testing pipelines, manage model versions, and implement guardrails to ensure your AI applications are both performant and responsible.\n\nWhat you'll learn:\n- Understand foundational LLM concepts, hyperparameter tuning, and model evaluation metrics\n- Configure Triton Inference Server to optimize model throughput and minimize latency\n- Implement version control and A/B testing methodologies for seamless model updates\n- Apply ethical AI frameworks, safety guardrails, and bias mitigation strategies in production\n- Explore specialized domain models, including BioNeMo for biotechnology applications\n- Design evaluation pipelines using modern MLOps best practices to track model drift\n\nThe curriculum begins with core definitions and evaluation concepts before progressing to practical deployment setups and advanced inference optimization. You will then explore real-world ethical considerations and safety frameworks to ensure responsible deployment.\n\nThis course is designed for beginners, developers, and product managers who want to understand the operational side of LLMs. No prior AI deployment experience is required.\n\nStart building reliable, high-performance, and ethical AI systems today.