LLMOps: Building Real-World Applications With Large Language Models

Overview

Understand and apply LLMOps principles to large language models. Master the ability to fine-tune, deploy, and monitor applications while implementing AI safety measures across multiple production projects.

Syllabus

Introduction to LLMOps

In this lesson, we will introduce LLMs and LLMOps, discuss the importance of LLMOps for real-world applications, overview the LLMOps lifecycle, and explain the difference between LLMOps and MLOps.

Working with LLMs

In this lesson, we will strategize around model training and selection, fine-tune and improve LLMs with experiment tracking, revise evaluation approaches for LLMs, and explore prompt engineering.

LLMOps in Practice

In this lesson, we will learn about model versioning and experiment management, explore different strategies for debugging LLMs, and deploy, monitor, and maintain LLMs in production.

Case Studies & Applications of LLMOps

In this lesson, we will explore several real world applications of LLMs, build a reliable customer support chatbot, build an LLM-based evaluation system, and implement a clickbait detector.

Advanced Topics in LLMs & LLMOps

In this lesson, we will explore challenges and strategies pertaining to running LLMs at scale, dive into safety and privacy concerns in AI, and learn about adversarial prompting and AI security.