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
- LLMOps vs. MLOps: Understanding the unique challenges of operating LLMs.
- The LLM application lifecycle: from prompt to evaluate, deploy, and 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.
- Implementing quality gates in CI/CD 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.
- Conducting red-teaming exercises for LLM applications to ensure safety.
LLM Observability and Monitoring
- Telmetry: tracking token usage, latency, cost, and quality metrics.
- Detecting drift in LLM outputs and embedding spaces.
- Session-level tracing for multi-turn agent conversations.
- Creating dashboards and setting up alerts with 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 techniques.
- Rate limiting, quota management, and API key governance.
Performance Optimization
- Semantic caching using vector stores and exact-match strategies.
- Enforcing structured outputs through constrained decoding.
- Implementing batching, streaming, and concurrency patterns.
- Optimizing latency across various model providers.
Governance, Compliance, and Audit
- LLM audit trails: logging prompts, responses, and decision provenance.
- Addressing data residency and privacy concerns for LLM APIs.
- Implementing policy-as-code for LLM usage within organizations.
- Developing an internal playbook for LLM operations.
Requirements
- Experience in building or integrating LLM-powered applications.
- Familiarity with Python and REST APIs.
- Basic understanding of prompt engineering concepts.
Audience
- ML engineers and MLOps practitioners transitioning to LLM operations.
- Platform engineers responsible for LLM infrastructure.
- Technical leads overseeing 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
