Skill Course
Production AI Engineering & LLMOps
Deploy, evaluate, observe, secure, and improve AI systems in production.
Learning objectives
Create offline and online AI evaluation systems
Trace model, retrieval and tool behavior
Deploy scalable and resilient AI services
Apply security, privacy and guardrail controls
Monitor quality, latency, cost and operational risk
Course coverage
Production AI architecture, AI gateways and services, and synchronous versus asynchronous workloads
AI evaluation, golden datasets, regression testing, quality metrics and human evaluation
Prompt and model versioning, experimentation, release management and AI CI/CD
Guardrails, structured validation, content filtering, fallbacks and failure handling
AI security, prompt-injection defense, PII and privacy, secrets and access control
Observability, tracing, logs, token usage, latency, cost per query and quality monitoring
Caching, retries, timeouts, rate limiting, model routing and fallback strategies
Docker, cloud deployment, managed AI services and production configuration
Scaling, queues, concurrency, streaming, performance and cost optimization
Production incidents, monitoring, feedback loops and continuous AI-system improvement