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Course Outline
Introduction to Large Language Model Operations (LLMOps)
- Distinguishing LLMOps from traditional MLOps: addressing the distinct operational challenges posed by large language models.
- Overview of the LLM application lifecycle, encompassing prompt design, evaluation, deployment, and ongoing monitoring.
- Comprehensive checklist to ensure production readiness for generative AI applications within federal and public sector environments.
Prompt Management and Version Control
- Implementation of prompt templating systems and dynamic variable injection mechanisms.
- Application of semantic versioning standards for prompts, supported by automated regression testing protocols.
- Establishment of centralized prompt registries to facilitate team collaboration and standardized workflows.
Large-Scale LLM Evaluation Frameworks
- Key evaluation dimensions: accuracy, relevance, safety compliance, and grounding in source data.
- Utilization of both LLM-as-a-judge metrics and structured human evaluation pipelines for rigorous assessment.
- Deployment of automated evaluation frameworks, including RAGAS, DeepEval, and custom evaluators tailored to specific operational needs.
- Integration of quality gates within continuous integration/continuous deployment (CI/CD) pipelines to validate LLM deployments.
Safety Guardrails and Content Governance
- Implementation of input and output guardrails using tools such as NeMo Guardrails and Guardrails AI to ensure adherence to policy.
- Deployment of personal identifiable information (PII) detection, toxicity filtering, and topical boundary enforcement.
- Strategies for defending against jailbreak attempts and prompt injection attacks.
- Conducting red-teaming exercises for LLM applications to verify safety assurance and risk mitigation.
LLM Observability and Performance Monitoring
- Collection of telemetry data, including token consumption, system latency, operational cost, and quality indicators.
- Detection of statistical drift in LLM outputs and embedding spaces to maintain model integrity.
- Session-level tracing capabilities for analyzing multi-turn agent interactions and conversation flows.
- Configuration of dashboards and alerting systems using LangSmith, Arize, and OpenTelemetry to support continuous oversight.
AI Gateway and Model Orchestration
- Routing logic for multi-provider model access utilizing platforms such as LiteLLM and Portkey.
- Implementation of fallback strategies, automated retry logic, and circuit breaker patterns to enhance system reliability.
- Cost-aware model selection mechanisms and dynamic load balancing techniques.
- Enforcement of rate limits, quota management, and API key governance protocols for secure access control.
Performance Optimization Strategies
- Implementation of semantic caching using vector stores alongside exact-match strategies to reduce redundant processing.
- Enforcement of structured outputs through constrained decoding techniques.
- Optimization of throughput via batching, streaming, and concurrency management patterns.
- Strategies for minimizing latency across diverse model providers to ensure responsive service delivery.
Governance, Regulatory Compliance, and Auditing
- Maintenance of comprehensive LLM audit trails, including prompt logs, response records, and decision provenance documentation.
- Assessment of data residency requirements and privacy considerations for LLM API integrations, particularly for government use cases.
- Development of policy-as-code frameworks to govern LLM usage across organizational boundaries.
- Development of internal LLM operations playbooks to standardize procedures and ensure accountability.
Requirements
- Proven track record in the development or integration of large language model-driven solutions.
- Competency in Python programming and the implementation of RESTful application programming interfaces.
- Fundamental knowledge of prompt engineering methodologies.
Target Audience
- Machine learning engineers and MLOps professionals advancing into the domain of large language model operations for government initiatives.
- Platform engineers tasked with the oversight and maintenance of large language model infrastructure.
- Technical leadership responsible for managing production-grade generative AI deployments.
14 Hours
