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Course Outline
Introduction to LLMOps
- Distinctions between LLMOps and MLOps: addressing the specific operational challenges of large language models.
- The lifecycle of LLM applications: encompassing prompt design, evaluation, deployment, and ongoing monitoring.
- Production readiness criteria for generative AI systems.
Prompt Management and Versioning
- Implementation of prompt templating architectures and variable injection mechanisms.
- Application of semantic versioning for prompts coupled with automated regression testing protocols.
- Establishment of prompt registries and structured collaboration workflows.
LLM Evaluation at Scale
- Assessment dimensions including accuracy, relevance, safety, and groundedness.
- Integration of LLM-as-judge metrics within human evaluation pipelines.
- Deployment of automated evaluation frameworks, such as RAGAS and DeepEval, alongside custom evaluators designed for government use cases.
- Implementation of quality assurance gates within CI/CD pipelines for LLM deployments.
Safety Guardrails and Content Governance
- Application of input and output guardrails, utilizing tools such as NeMo Guardrails and Guardrails AI.
- Implementation of personally identifiable information (PII) detection, toxicity filtering, and topic boundary controls.
- Strategies for defending against jailbreak attempts and prompt injection attacks.
- Execution of red-team exercises to ensure safety assurance in LLM applications.
LLM Observability and Monitoring
- Collection of telemetry data, including token consumption, latency, cost, and quality indicators.
- Detection of drift in LLM outputs and embedding spaces.
- Session-level tracing for the analysis of multi-turn agent interactions.
- Configuration of dashboards and alerting systems using LangSmith, Arize, and OpenTelemetry.
AI Gateway and Model Orchestration
- Multimodel routing capabilities via LiteLLM and Portkey.
- Deployment of fallback mechanisms, retry logic, and circuit breaker patterns.
- Cost-efficient model selection and load balancing strategies.
- Governance of rate limits, quotas, and API keys.
Performance Optimization
- Semantic caching techniques leveraging vector stores and exact-match methods.
- Enforcement of structured outputs through constrained decoding.
- Implementation of batching, streaming, and concurrency patterns.
- Latency reduction strategies across diverse model providers.
Governance, Compliance, and Audit
- Maintenance of LLM audit trails, including prompt and response logs for decision provenance.
- Adherence to data residency requirements and privacy protocols for LLM APIs in government contexts.
- Implementation of policy-as-code frameworks for organizational LLM usage.
- Development of internal standard operating procedures for LLM operations.
Requirements
- Demonstrated proficiency in developing or integrating large language model-driven applications.
- Working knowledge of Python programming and RESTful application programming interfaces.
- Fundamental comprehension of prompt engineering principles.
Audience
- Machine learning engineers and MLOps professionals advancing into LLM operations.
- Platform engineers tasked with maintaining LLM infrastructure for government use.
- Technical leadership overseeing the production deployment of generative AI systems.
14 Hours
Testimonials (1)
use of proper and effective prompt
