LLMOps: Production LLM Operations and Governance Training Course

Introduction to LLMOps

• Distinguishing LLMOps from MLOps: specific challenges in operating LLMs
• The LLM application lifecycle: covering prompt, evaluate, deploy, and monitor phases
• Checklist for ensuring production readiness in GenAI applications

Prompt Management and Versioning

• Implementing prompt templating systems and variable injection
• Applying semantic versioning to prompts alongside automated regression testing
• Utilizing prompt registries and defining collaboration workflows

LLM Evaluation at Scale

• Key evaluation dimensions: accuracy, relevance, safety, and groundedness
• Utilizing LLM-as-judge metrics and establishing human evaluation pipelines
• Employing automated eval frameworks such as RAGAS, DeepEval, and custom evaluators
• Implementing quality gates within CI/CD processes for LLM deployments

Safety Guardrails and Content Governance

• Implementing input and output guardrails using NeMo Guardrails and Guardrails AI
• Managing PII detection, toxicity filtering, and defining topic boundaries
• Strategies for defending against jailbreaks and prompt injection
• Conducting red-teaming exercises for LLM applications to ensure safety

LLM Observability and Monitoring

• Tracking telemetry data including token usage, latency, cost, and quality metrics
• Detecting drift in LLM outputs and embedding spaces
• Performing session-level tracing for multi-turn agent conversations
• Configuring dashboards and alerting systems using LangSmith, Arize, and OpenTelemetry

AI Gateway and Model Orchestration

• Implementing multi-provider routing with LiteLLM and Portkey
• Defining fallback strategies, retry logic, and circuit breakers
• Executing cost-aware model selection and load balancing
• Managing rate limiting, quotas, and API key governance

Performance Optimization

• Implementing semantic caching with vector stores and exact-match strategies
• Enforcing structured outputs through constrained decoding
• Applying batching, streaming, and concurrency patterns
• Optimizing latency across different model providers

Governance, Compliance, and Audit

• Maintaining LLM audit trails: including prompt logs, response logs, and decision provenance
• Addressing data residency and privacy considerations for LLM APIs
• Implementing policy-as-code for internal LLM usage
• Developing a comprehensive internal LLM operations playbook