LLMOps: Building Real-World Applications With Large Language Models

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.

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.

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.

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.

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.

In this lesson, we will take a high-level view of LLMOps trends, look towards the future of LLMs and LLMOps, and explore the broader MLOps landscape.