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Duration 14 hours
Course Outline
Introduction to LLMOps
- Comparison of LLMOps and MLOps: Specific operational complexities associated with Large Language Models (LLMs)
- The lifecycle of LLM applications: Prompting, evaluation, deployment, and continuous monitoring
- Standards for production readiness in Generative AI systems
Prompt Management and Versioning
- Architecting prompt templates and managing dynamic variable integration
- Applying semantic versioning to prompts supported by automated regression testing protocols
- Establishing prompt registries and structured collaboration workflows
Large-Scale LLM Evaluation
- Key evaluation metrics: Accuracy, relevance, safety compliance, and factual groundedness
- Implementing LLM-as-judge methodologies alongside human-in-the-loop evaluation pipelines
- Utilizing automated evaluation frameworks such as RAGAS, DeepEval, and custom assessment tools
- Integrating quality gates into CI/CD pipelines for LLM release processes
Safety Guardrails and Content Governance
- Configuring input and output safety controls using NeMo Guardrails and Guardrails AI
- Enforcing Personal Identifiable Information (PII) detection, toxicity filtering, and topic restrictions
- Mitigation strategies for jailbreak attempts and prompt injection attacks
- Conducting red-teaming exercises to ensure LLM application security
LLM Observability and Monitoring
- Tracking telemetry data: Token consumption, response latency, operational costs, and performance quality
- Identifying drift in LLM output patterns and embedding vector spaces
- Implementing session-level tracing for complex, multi-turn agent interactions
- Deploying dashboards and alert systems via LangSmith, Arize, and OpenTelemetry
AI Gateway and Model Orchestration
- Implementing multi-provider routing using LiteLLM and Portkey
- Defining fallback mechanisms, retry logic, and circuit breaker patterns
- Optimizing model selection based on cost-efficiency and load balancing requirements
- Managing rate limits, resource quotas, and API key governance
Performance Optimization
- Utilizing semantic caching with vector stores and exact-match retrieval strategies
- Ensuring structured outputs through constrained decoding techniques
- Implementing batching, streaming, and high-concurrency processing patterns
- Reducing latency across various model service providers
Governance, Compliance, and Audit
- Maintaining comprehensive LLM audit trails: Prompt logs, response records, and decision provenance
- Addressing data residency and privacy regulations specific to LLM API interactions
- Implementing Policy-as-Code frameworks to regulate internal LLM usage
- Developing internal operational playbooks for LLM management
Requirements
- Professional experience in developing or integrating LLM-based applications.
- Proficiency in Python and RESTful API interactions.
- Fundamental understanding of prompt engineering principles.
Target Audience
- Machine learning engineers and MLOps practitioners expanding their expertise into LLM operations.
- Platform engineers overseeing LLM infrastructure and deployment.
- Technical leads managing production environments for Generative AI systems.
Testimonials (1)
use of proper and effective prompt
