MLOPs and LLMOPs

New in This Release: LLMOps Content and Labs

In this release version, we are excited to announce that LLMOps has been added to the content and labs.
This enhancement expands the curriculum beyond core MLOps to include specialized practices for managing large language models (LLMs) in production.

Students will now gain hands-on experience with topics such as:

• Model evaluation and monitoring
• Alignment and responsible AI practices
• Deployment strategies tailored for LLMs
• Lifecycle management at scale

The labs have also been updated to provide practical, real-world exercises that demonstrate end-to-end LLMOps workflows. This ensures participants not only understand the concepts but also build the technical skills needed to operate and maintain LLM systems in production environments.

What's Changed

• LLMOps course content and labs added by @raminmohammadi in #70
• vertex AI lab 2 folder merge by @rahulodedra30 in #63
• KServer FInal Labs by @HeyIts-RJ in #66
• Kubeflow Lab 3 by @DRAJ6 in #65
• Airflow Dags deploy and trigger from GCP VM by @rahulodedra30 in #64
• reformated the repo by @raminmohammadi in #67
• Basic and Intermediate Cloud Runner Labs by @shaunkirthan in #68
• Terraform Labs by @gibran96 in #69

Full Changelog: V1.2.0...V2.0.0