LLMOps & MLOps

LLMOps:

Large Language Model Ops (LLMOps) encompasses the process, techniques and tools used for the operational management of the large language models in production environment.

In terms of MLOps, there are 4 key steps,

• data prep
• build and train
• deploy
• monitor

In terms of LLMOps, those key steps are same as MLOps,

• data prep
• build and train
• deploy
• monitor

But, key differences are Build and Train part.

under MLOps, it is,

• Feature Engineering
• Model & Algorithm
• Train Model
• Test Model
• Hyperparameter tuning

• For LLMOps, it is,
• Base model selection
• Prompt Engineering
• Fine-tuning
• RAG
• Hyperparameter tuning
• Repeatable pipelines

Why do we need LLMOps?

•LLM Development lifecycle

•Efficiency: faster model and pipeline development

•Scalability: vast scalability and management where tons of models can be managed.

•Risk Reduction: transparency, regulation, monitoring, drifting.

LLMOps Components

•Exploratory data analysis

•Data preparation and prompt engineering

•Model fine-tuning

•Introducing RAG

•Model review and governance

•Model inference and serving

•Model monitoring with human in the loop