Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to LLMOps
- Differences between LLMOps and MLOps: unique challenges of operating LLMs.
- The LLM application lifecycle: prompt, evaluate, deploy, monitor.
- Production readiness checklist for GenAI applications.
Prompt Management and Versioning
- Prompt templating systems and variable injection techniques.
- Semantic versioning for prompts combined with automated regression testing.
- Prompt registries and collaboration workflows.
LLM Evaluation at Scale
- Evaluation dimensions: accuracy, relevance, safety, and groundedness.
- LLM-as-judge metrics and human evaluation pipelines.
- Automated evaluation frameworks: RAGAS, DeepEval, and custom evaluators.
- Quality gates within CI/CD processes for LLM deployments.
Safety Guardrails and Content Governance
- Input and output guardrails: NeMo Guardrails and Guardrails AI.
- PII detection, toxicity filtering, and topic boundaries.
- Strategies for defending against jailbreaks and prompt injection.
- Red-teaming LLM applications to ensure safety assurance.
LLM Observability and Monitoring
- Telemetry: tracking token usage, latency, cost, and quality metrics.
- Drift detection in LLM outputs and embedding spaces.
- Session-level tracing for multi-turn agent conversations.
- Dashboards and alerting using LangSmith, Arize, and OpenTelemetry.
AI Gateway and Model Orchestration
- Multi-provider routing using LiteLLM and Portkey.
- Fallback strategies, retry logic, and circuit breakers.
- Cost-aware model selection and load balancing.
- Rate limiting, quota management, and API key governance.
Performance Optimization
- Semantic caching utilizing vector stores and exact-match strategies.
- Enforcing structured output via constrained decoding.
- Batching, streaming, and concurrency patterns.
- Latency optimization across various model providers.
Governance, Compliance, and Audit
- LLM audit trails: prompt logs, response logs, and decision provenance.
- Data residency and privacy considerations for LLM APIs.
- Policy-as-code for managing LLM usage within organizations.
- Developing an internal LLM operations playbook.
Requirements
- Experience in building or integrating LLM-powered applications.
- Familiarity with Python and REST APIs.
- A basic understanding of prompt engineering concepts.
Audience
- ML engineers and MLOps practitioners transitioning into LLM operations.
- Platform engineers responsible for LLM infrastructure.
- Technical leads managing production GenAI deployments.
14 Hours
Testimonials (2)
use of proper and effective prompt
Marses Pacaldo
Course - Generative AI and Prompt Engineering for Corporate Professionals
The interactive style, the exercises
