Lesson 1
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.
Free
LLMOps: Building Real-World Applications With Large Language Models
Course
The goal of this course is to teach you everything you need to build real-world, production software with large language models. Unlike other LLM-focused courses, our emphasis will not be on theory or on building simple proof-of-concept projects in Jupyter notebooks. We will instead be exploring the operations behind production-grade LLM applications. This includes, but is not limited to experiment management and model versioning, fine-tuning and prompt engineering, deployment, monitoring, and maintenance of LLMs, and AI safety and security (including how to protect against adversarial actors). Along the way, we'll build several full applications, including a chatbot, an evaluation system, and a clickbait detector. Finally, we'll look at the future of LLM and LLMOps.
The goal of this course is to teach you everything you need to build real-world, production software with large language models. Unlike other LLM-focused courses, our emphasis will not be on theory or on building simple proof-of-concept projects in Jupyter notebooks. We will instead be exploring the operations behind production-grade LLM applications. This includes, but is not limited to experiment management and model versioning, fine-tuning and prompt engineering, deployment, monitoring, and maintenance of LLMs, and AI safety and security (including how to protect against adversarial actors). Along the way, we'll build several full applications, including a chatbot, an evaluation system, and a clickbait detector. Finally, we'll look at the future of LLM and LLMOps.
Built in collaboration with
Comet
Intermediate
11 hours
Completion Certificate
Last Updated January 30, 2024
Large Language Models • Comet ML • MLOps
Basic machine learning • Intermediate Python
Course Lessons
Lesson 2
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.
Lesson 3
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.
Lesson 4
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.
Lesson 5
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.
Lesson 6
The Future of LLMOps
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.
Taught By The Best
Comet
Comet's mission is to empower practitioners and teams to achieve business value with AI. Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models. Organizations of every size—from academic teams to startups to enterprise companies—use Comet's platform to build better ML models faster.
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