LLMOps: Deploying and Monitoring Generative AI Systems

Course Category : Artificial Intelligence

An advanced programme for developing structured LLMOps capabilities across generative AI deployment, monitoring, reliability, governance, and operational lifecycle management.
Duration: 5 Days
Level: Advanced

Introduction

As generative AI moves from experimentation into enterprise systems, effective operational lifecycle management becomes critical to maintaining stability, quality, scalability, and control. LLMOps provides the operational discipline required to coordinate models, infrastructure, deployment processes, monitoring, governance, and continuous improvement.
This course examines the core principles and practices for deploying and operating large language model systems. It addresses operational architectures, version management, performance and quality monitoring, drift detection, cost and resource management, reliability, security, and governance, providing participants with a structured perspective on managing production-grade generative AI systems.

Targeted Audience

AI and Machine Learning Engineers
MLOps and LLMOps Engineers
DevOps and Cloud Platform Engineers
Data and Software Engineers working with AI solutions
Solution and Systems Architects
AI and Technical Team Leaders
AI Platform Operations and Monitoring Professionals
AI Governance and Risk Professionals

Targeted Skills

LLMOps Architecture and Methodology
Generative AI Deployment Planning
Model, Version, and Configuration Management
Performance, Quality, and Reliability Monitoring
Drift and Behavioural Change Analysis
Latency, Resource, and Cost Management
Large Language Model Lifecycle Management
Security and Governance Integration

Expected Outcomes

Explain the fundamental components of LLMOps and their relationship to the generative AI lifecycle.
Identify architectural and operational requirements for large language model deployment.
Structure model, version, and configuration management processes.
Define appropriate metrics for performance, quality, reliability, and cost monitoring.
Analyse drift, quality degradation, and operational failure risks.
Establish an integrated lifecycle management framework for generative AI systems.
Integrate security, governance, and accountability requirements into LLMOps.

Training Topics Index

LLMOps concepts and relationships with MLOps and DevOps
Large language model lifecycle components
Transitioning models from development to production
Operational architecture for generative AI systems
Scalability, reliability, and operational sustainability challenges

Large language model deployment architectures
Endpoint and inference service management
Model, version, and configuration management
Release, update, and rollback strategies
Dependency, environment, and infrastructure management

Operational performance metrics for language models
Latency, availability, and error-rate monitoring
Output quality, consistency, and relevance evaluation
Drift and input-output behavioural change detection
Logging, alerting, and end-to-end observability

Compute, token consumption, and cost management
Capacity planning and AI workload scalability
Failure management, continuity, and recovery
Input, output, and model-access risks
Security controls, secrets, and permissions management

Model governance, ownership, and accountability
Traceability, documentation, and version history
Risk management and compliance for generative AI environments
Using monitoring data to support optimisation decisions
Establishing a sustainable LLM lifecycle operating framework

Course Features

Updated and Interactive Content
Hypothetical Examples and Case Studies
Pre- and Post-assessments to Measure Impact
Verified Certificate with a QR Verification Code

LLMOps: Deploying and Monitoring Generative AI Systems