Mlops Pipeline.prompt

# MLOps Pipeline for LLMs

## Overview

MLOps pipelines automate the lifecycle of LLM systems from training through deployment and monitoring. A production MLOps pipeline includes data preparation, model training, evaluation, deployment, monitoring, and retraining stages. Each stage must be automated, versioned, and reproducible. The pipeline must handle the unique challenges of LLMs: large model sizes, expensive training, complex evaluation, and continuous monitoring for drift and safety.

## Key Concepts

- **Pipeline Orchestration**: Automating multi-stage ML workflows (Airflow, Prefect, Kubeflow)
- **Model Registry**: Versioned storage for trained models (MLflow, Weights & Biases)
- **Experiment Tracking**: Logging training runs, hyperparameters, and metrics
- **Continuous Training**: Automated retraining when data or performance changes
- **Model Serving**: Deploying models for inference (vLLM, TGI, Triton)
- **Feature Store**: Centralized feature management for ML pipelines
- **Data Versioning**: Tracking dataset versions (DVC, LakeFS)

## Implementation Patterns

### LLM Training Pipeline