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 with automated regression testing.
- Prompt registries and collaboration workflows.
LLM Evaluation at Scale
- Evaluation dimensions: accuracy, relevance, safety, and groundedness.
- Using LLM-as-judge metrics and human evaluation pipelines.
- Automated evaluation frameworks: RAGAS, DeepEval, and custom evaluators.
- Integrating quality gates in 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 defining topic boundaries.
- Strategies for defending against jailbreaks and prompt injection attacks.
- Conducting red-teaming of LLM applications to ensure safety assurance.
LLM Observability and Monitoring
- Telometry tracking: token usage, latency, cost, and quality metrics.
- Detecting drift in LLM outputs and embedding spaces.
- Session-level tracing for multi-turn agent conversations.
- Setting up dashboards and alerting using LangSmith, Arize, and OpenTelemetry.
AI Gateway and Model Orchestration
- Multi-provider routing using LiteLLM and Portkey.
- Implementing fallback strategies, retry logic, and circuit breakers.
- Cost-aware model selection and load balancing techniques.
- Rate limiting, quota management, and API key governance.
Performance Optimization
- Semantic caching using vector stores and exact-match strategies.
- Enforcing structured output with constrained decoding.
- Utilizing batching, streaming, and concurrency patterns.
- Optimizing latency across different model providers.
Governance, Compliance, and Audit
- LLM audit trails: prompt logs, response logs, and decision provenance.
- Data residency and privacy considerations for LLM APIs.
- Implementing Policy-as-code for organizational LLM usage.
- Developing an internal playbook for LLM operations.
Requirements
- Experience in building or integrating applications powered by large language models.
- Familiarity with Python and REST APIs.
- A foundational understanding of prompt engineering concepts.
Target Audience
- ML engineers and MLOps practitioners who are transitioning to LLM operations.
- Platform engineers responsible for managing LLM infrastructure.
- Technical leads overseeing production Generative AI 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
