Generative AI and LLMOps: Deploying & Managing LLMs in Production

This is the repository for the LinkedIn Learning course Generative AI and LLMOps: Deploying & Managing LLMs in Production. The full course is available from LinkedIn Learning.

Cutting-edge artificial intelligence technologies are changing the world. But without proper deployment and management, your applications may never reach their full potential. Worse, they could simply fail or even cause critical errors in your systems. As more organizations are incorporating large language models into their workflows, there's an increasing need for professionals skilled in deploying and monitoring these models effectively, responsibly, and securely in production environments. In this course, learn the advanced techniques and best practices for deploying and monitoring LLMs in production environments. Explore LLM deployment options, handling API limitations, performance monitoring techniques, prompt management, addressing hallucinations, and more. Plus, learn about security and cost considerations, and test your learning with challenges and solutions.

See the readme file in the main branch for updated instructions and information.

Instructions

This repository has branches for each of the videos in the course. You can use the branch pop up menu in github to switch to a specific branch and take a look at the course at that stage, or you can add /tree/BRANCH_NAME to the URL to go to the branch you want to access.

Branches

The branches are structured to correspond to the videos in the course. The naming convention is CHAPTER#_MOVIE#. As an example, the branch named 02_03 corresponds to the second chapter and the third video in that chapter. Some branches will have a beginning and an end state. These are marked with the letters b for "beginning" and e for "end". The b branch contains the code as it is at the beginning of the movie. The e branch contains the code as it is at the end of the movie. The main branch holds the final state of the code when in the course.

When switching from one exercise files branch to the next after making changes to the files, you may get a message like this:

error: Your local changes to the following files would be overwritten by checkout:        [files]
Please commit your changes or stash them before you switch branches.
Aborting

To resolve this issue:

Add changes to git using this command: git add .
Commit changes using this command: git commit -m "some message"