Advanced LLMOps Systems and Workflows
Building upon the core operations of training, deployment, and monitoring covered previously, this chapter addresses the integration of these elements into broader, automated systems and sophisticated workflows specific to large models.
You will learn practical methods for operationalizing prompt engineering, managing the components of Retrieval-Augmented Generation (RAG) systems including vector databases, and constructing automated pipelines for model retraining or fine-tuning. Additionally, we will examine security considerations pertinent to LLMOps, approaches to governance and compliance for LLM deployments, and strategies for connecting these specialized workflows with standard Continuous Integration and Continuous Deployment (CI/CD) practices. The focus is on assembling the operational components into efficient, end-to-end LLM systems.
Sections
6.1 Operationalizing Prompt Engineering
6.2 Managing Retrieval-Augmented Generation (RAG) Systems
6.3 Vector Database Operations and Management
6.4 Automating LLM Retraining and Fine-tuning Pipelines
6.5 Security Considerations in LLMOps
6.6 Compliance and Governance in LLM Deployments
6.7 Integrating LLMOps with CI/CD Systems
6.8 Practice: Building a Prompt Management Workflow
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