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In this course, you will learn:
- Introduce LLMs and LLMOps, examine their usefulness in real-world applications, provide an overview of the LLMOps lifecycle, and explain the distinction between LLMOps and MLOps.
- We will strategize around model training and selection, fine-tune and improve LLMs through experiment tracking, rewrite LLM evaluation methodologies, and investigate rapid engineering.
- Learn about model versioning and experiment management, as well as several debugging methodologies for LLMs. Finally, deploy, monitor, and maintain LLMs in production.
- Investigate many real-world applications of LLMs, develop a dependable customer assistance chatbot, create an LLM-based evaluation system, and install a clickbait detector.
- Explore the problems and solutions for executing LLMs at scale, delving into AI safety and privacy concerns, and learning about adversarial prompting and security.
- will provide a high-level overview of LLMOps trends, forecast the future of LLMs and LLMOps, and investigate the larger MLOps ecosystem.
Syllabus:
- Introduction to LLMOps
- Working with LLMs
- LLMOps in Practice
- Case Studies & Applications of LLMOps
- Advanced Topics in LLMs & LLMOps
- The Future of LLMOps
