LLMOps Certification

Module 1: Introduction to LLMOps

• Understanding LLMOps
• LLMOps versus traditional MLOps
• LLM application lifecycle
• Operational challenges
• Production AI requirements

Module 2: LLM Application Architecture

• Application layer
• Model layer
• Retrieval layer
• Integration layer
• Infrastructure layer

Module 3: LLM Development Lifecycle

• Requirements
• Development
• Testing
• Deployment
• Continuous improvement

Module 4: Development and Production Environments

• Environment separation
• Configuration management
• Development environments
• Staging environments
• Production environments

Module 5: Model Management

• Model selection
• Hosted models
• Self-hosted models
• Model configuration
• Managing model dependencies

Module 6: Model Versioning

• Tracking model versions
• Comparing model changes
• Version metadata
• Rollback strategies
• Model lifecycle management

Module 7: Prompt Management

• Prompt templates
• Prompt repositories
• Prompt variables
• Managing prompt changes
• Reusable prompt components

Module 8: Prompt Versioning

• Tracking prompt versions
• Comparing prompt performance
• Testing prompt changes
• Rollback approaches
• Change documentation

Module 9: Configuration Management

• Application configuration
• Model parameters
• Environment variables
• Feature configuration
• Configuration versioning

Module 10: Secrets and Credential Management

• API credentials
• Environment secrets
• Access controls
• Secret rotation
• Protecting sensitive configuration

Module 11: LLM Evaluation Fundamentals

• Evaluation objectives
• Quality dimensions
• Task completion
• Accuracy and relevance
• Evaluation datasets

Module 12: Automated Evaluation Pipelines

• Test datasets
• Evaluation workflows
• Model-based evaluation
• Regression testing
• Comparing application versions

Module 13: Human Evaluation

• Evaluation rubrics
• Human review workflows
• Response comparison
• Reviewer consistency
• Feedback analysis

Module 14: LLM Observability

• Understanding observability
• Model interactions
• Application behaviour
• Operational visibility
• Observability workflows

Module 15: Tracing LLM Applications

• Request traces
• Model calls
• Retrieval steps
• Tool calls
• End-to-end execution paths

Module 16: Logging

• Application logs
• Model request logs
• Error logs
• Structured logging
• Log management

Module 17: Monitoring LLM Applications

• Application availability
• Response latency
• Error rates
• Token consumption
• Usage patterns

Module 18: Quality Monitoring

• Response relevance
• Groundedness
• Output consistency
• Failure patterns
• Quality trends

Module 19: Hallucination Monitoring

• Identifying unsupported outputs
• Grounding checks
• Retrieval verification
• Failure analysis
• Mitigation strategies

Module 20: Retrieval Operations

• Retrieval pipelines
• Knowledge sources
• Index management
• Retrieval configuration
• Retrieval monitoring

Module 21: RAG Operations

• Document ingestion
• Chunking pipelines
• Embedding generation
• Vector indexing
• Knowledge refresh workflows

Module 22: Monitoring RAG Quality

• Retrieval relevance
• Context quality
• Missing information
• Stale knowledge
• Retrieval performance

Module 23: Embedding and Vector Store Management

• Embedding models
• Vector indexes
• Metadata management
• Re-indexing
• Version considerations

Module 24: Data Pipeline Operations

• Data ingestion
• Data transformation
• Validation
• Pipeline scheduling
• Failure recovery

Module 25: LLM Application Testing

• Unit testing
• Integration testing
• Scenario testing
• Edge cases
• Regression testing

Module 26: Production Validation

• Pre-deployment checks
• Quality thresholds
• Performance validation
• Security validation
• Release readiness

Module 27: CI/CD for LLM Applications

• Continuous integration
• Automated testing
• Deployment pipelines
• Release workflows
• Deployment controls

Module 28: Deployment Strategies

• Rolling deployments
• Blue-green deployments
• Canary releases
• Feature flags
• Rollback planning

Module 29: Containerization

• Container concepts
• Packaging AI applications
• Dependencies
• Environment consistency
• Container deployment

Module 30: Cloud Deployment

• Cloud infrastructure
• Managed AI services
• Compute resources
• Storage
• Networking considerations

Module 31: Scaling LLM Applications

• Concurrent requests
• Horizontal scaling
• Queue-based workloads
• Load management
• Capacity planning

Module 32: Rate Limit Management

• Provider rate limits
• Request throttling
• Queuing
• Backoff strategies
• Managing traffic spikes

Module 33: Latency Optimization

• Measuring latency
• Model selection
• Streaming responses
• Parallel execution
• Reducing processing overhead

Module 34: Cost Management

• Token consumption
• Model pricing considerations
• Usage tracking
• Cost allocation
• Cost optimization strategies

Module 35: Caching Strategies

• Response caching
• Semantic caching
• Retrieval caching
• Cache invalidation
• Cost and latency benefits

Module 36: Reliability Engineering

• Availability
• Fault tolerance
• Retry strategies
• Timeouts
• Graceful degradation

Module 37: Failure Handling and Recovery

• Model provider failures
• API failures
• Retrieval failures
• Fallback models
• Recovery workflows

Module 38: LLM Security Operations

• Access management
• API security
• Credential protection
• Data security
• Secure operational practices

Module 39: Prompt Injection Monitoring

• Prompt injection risks
• Untrusted inputs
• Suspicious patterns
• Tool restrictions
• Response controls

Module 40: Data Privacy and Governance

• Sensitive information
• Data minimization
• Retention considerations
• Access controls
• Operational governance

Module 41: Guardrails in Production

• Input guardrails
• Output guardrails
• Application boundaries
• Tool permissions
• Escalation mechanisms

Module 42: Incident Management

• Detecting incidents
• Incident classification
• Escalation
• Containment
• Post-incident analysis

Module 43: Service-Level Objectives

• Reliability targets
• Availability indicators
• Latency indicators
• Quality indicators
• Monitoring operational targets

Module 44: Usage Analytics

• User activity
• Feature usage
• Model usage
• Request patterns
• Operational insights

Module 45: Feedback Loops

• User feedback
• Quality feedback
• Failure analysis
• Improvement prioritization
• Feeding insights into development

Module 46: Production Optimization

• Reviewing performance
• Improving prompts
• Improving retrieval
• Optimizing infrastructure
• Managing model changes

Module 47: Designing an LLMOps Platform

• Platform requirements
• Model management
• Evaluation infrastructure
• Observability architecture
• Deployment workflows

Module 48: Building an End-to-End LLMOps Strategy

• Operational requirements
• Release processes
• Monitoring framework
• Reliability and security
• Continuous improvement