Certified LLM Ops Professional

•Introduction to LLMOps and its evolution from MLOps

•Characteristics of LLM systems: non-determinism, prompt sensitivity, and token economics

•LLMOps lifecycle and operational workflows

•Key roles and responsibilities in LLMOps

•Build vs. buy decisions for LLM solutions

•Challenges in operationalizing LLM applications

•Proprietary vs. open-weight LLMs and licensing considerations

•LLM model selection criteria

•Inference serving architectures and frameworks

•vLLM, TGI, Triton, and Ray Serve

•GPU sizing and resource optimization

•Model quantization: INT8, INT4, GPTQ, and AWQ

•Latency, throughput, batching, and inference optimization

•Production-grade prompt engineering

•Prompt templates, versioning, and management

•System prompt design

•Structured outputs and JSON mode

•Function calling and tool use

•Context window management and optimization

•Chunking and context compression strategies

•Prompt testing and regression management

•RAG architecture and core concepts

•Embedding models and vector representations

•Vector database selection and architecture

•pgvector, Pinecone, Weaviate, and Qdrant

•Document ingestion, chunking, and indexing

•Semantic, keyword, and hybrid search

•Re-ranking and retrieval optimization

•Data freshness and access control

•Common RAG challenges and failure modes

•Prompting vs. RAG vs. fine-tuning

•Fine-tuning strategies and use cases

•Supervised Fine-Tuning (SFT)

•Parameter-Efficient Fine-Tuning (PEFT)

•LoRA and QLoRA

•Dataset curation and preparation

•Synthetic data generation

•RLHF and DPO fundamentals

•Fine-tuning evaluation and production considerations

•LLM evaluation principles and methodologies

•Golden datasets and evaluation harnesses

•Rubric-based evaluation

•LLM-as-a-Judge

•Measuring accuracy, relevance, and task success

•Hallucination and toxicity evaluation

•Offline and online evaluation

•A/B testing and shadow deployments

•Regression testing and continuous evaluation

•Automated quality gates for production deployments

•Production architecture for LLM applications

•CI/CD for prompts, models, and RAG indexes

•Docker and containerization

•Kubernetes for LLM workloads

•KServe and Seldon

•Model registries and version management

•Deployment strategies for LLM applications

•Caching and performance optimization

•Integration of LLMOps with MLOps workflows

•LLM application observability fundamentals

•Logging and distributed tracing

•OpenTelemetry for LLM systems

•Monitoring latency, errors, throughput, and token usage

•Input and output quality monitoring

•Model and data drift detection

•Cost tracking and token-level cost analysis

•Model routing and cost optimization

•Alerting and incident management

•LLM performance optimization

•LLM security fundamentals and threat landscape

•Prompt injection and jailbreak attacks

•Data leakage and insecure tool use

•PII detection and redaction

•Privacy and compliance considerations

•GDPR and SOC 2 requirements

•LLM guardrails and safety controls

•Access control and authentication

•Audit logging and secure context handling

•LLM red-teaming and security testing

•AI governance and risk management

•Scaling LLM applications for production

•Multi-model architecture and model routing

•Model fallback and resilience strategies

•Cost-aware model selection

•Agentic systems and production considerations

•Tool-calling reliability and orchestration

•Failure recovery and retry mechanisms

•Disaster recovery and high availability

•Platform team patterns for LLMOps

•End-to-end production readiness

•Capstone: Design and build a production-ready LLM application