Description

This curriculum spans the equivalent depth and breadth of a multi-workshop program for designing, operating, and governing AI-integrated systems where availability depends on coordinated infrastructure, data, model, and compliance practices across distributed environments.

Module 1: Defining Availability Objectives in Complex Enterprise Systems

Establishing service-level objectives (SLOs) for mission-critical AI inference endpoints based on business impact analysis
Negotiating availability targets with stakeholders when infrastructure constraints limit redundancy options
Translating regulatory uptime requirements into measurable SLIs for AI-powered compliance systems
Aligning availability goals across hybrid cloud and on-premises AI model deployments
Defining failure thresholds for AI model serving systems where partial degradation affects accuracy
Integrating availability objectives into AI model lifecycle planning from development to retirement
Handling conflicting availability demands between real-time inference and batch processing workloads
Documenting fallback behaviors for AI services during partial outages to maintain core functionality

Module 2: Architecting Resilient AI Infrastructure

Selecting between active-active and active-passive configurations for distributed AI inference clusters
Designing GPU node failover strategies that account for model warm-up and memory state
Implementing anti-affinity rules to prevent co-location of AI model replicas on shared hardware
Configuring persistent storage replication for AI model checkpoints across availability zones
Planning for burst capacity in AI inference systems during traffic spikes without over-provisioning
Integrating third-party AI hardware accelerators into high-availability cluster designs
Designing network topology to minimize latency between AI model servers and data sources during failover
Validating infrastructure-as-code templates for consistent deployment of redundant AI environments

Module 3: Fault Detection and Automated Recovery in AI Systems

Developing health checks that detect model performance drift as a precursor to failure
Configuring anomaly detection on AI inference latency and error rates to trigger automated remediation
Implementing circuit breaker patterns for AI microservices that degrade gracefully under load
Designing rollback mechanisms for AI model updates that fail post-deployment validation
Setting up distributed tracing to isolate failure points in multi-stage AI pipelines
Automating retraining pipeline recovery when data source connectivity is restored
Coordinating alerting thresholds across monitoring layers to avoid alert storms during cascading failures
Validating failover automation through controlled chaos engineering experiments on AI clusters

Module 4: Data Availability and Consistency in AI Workflows

Designing data replication strategies for training datasets across geographically distributed sites
Handling data schema drift in streaming pipelines that feed real-time AI models
Implementing data versioning to ensure reproducibility during AI model recovery operations
Configuring caching layers for high-frequency AI inference requests without stale data exposure
Managing data access permissions during failover to maintain compliance in replicated environments
Reconciling data consistency between primary and backup systems after network partition recovery
Planning for data backfill procedures when AI pipeline components are restored after downtime
Integrating data quality monitoring as a prerequisite for AI model reactivation post-outage

Module 5: AI Model Deployment and Versioning Strategies

Implementing canary deployments for AI models with automated rollback based on performance metrics
Managing model version coexistence during transition periods in high-availability environments
Designing A/B testing frameworks that do not compromise system availability
Storing model artifacts in version-controlled repositories with access controls and retention policies
Coordinating model updates with dependent services to prevent interface mismatches
Validating model compatibility with inference hardware before promoting to production
Handling stateful model updates where internal parameters must be preserved during rollout
Documenting model dependencies and environment requirements to support rapid redeployment

Module 6: Disaster Recovery Planning for AI-Centric Systems

Defining recovery time and point objectives (RTO/RPO) for AI training and inference systems
Conducting disaster recovery drills that simulate complete data center outages for AI clusters
Storing encrypted model weights and configurations in geographically isolated backup locations
Validating backup integrity for large AI model files using checksum verification routines
Documenting manual intervention procedures when automated recovery fails for AI services
Coordinating failover testing with external data providers that supply real-time inputs to AI models
Managing licensing constraints for AI software during unplanned activation of backup environments
Updating disaster recovery playbooks to reflect changes in model architecture and dependencies

Module 7: Monitoring and Observability for AI Availability

Instrumenting AI inference endpoints to capture request-level success, latency, and resource usage
Correlating infrastructure metrics with model performance indicators to identify root causes
Designing dashboards that highlight availability trends specific to AI workloads
Setting up alerts for data distribution shifts that may degrade model reliability
Implementing log aggregation across distributed AI components with consistent tagging
Using synthetic transactions to verify end-to-end availability of AI-powered workflows
Archiving observability data to support post-incident analysis and compliance audits
Enforcing access controls on monitoring systems to prevent exposure of sensitive model behavior

Module 8: Governance and Compliance in AI Availability Management

Documenting availability controls to meet industry-specific regulatory requirements for AI systems
Conducting third-party audits of AI disaster recovery procedures and evidence retention
Managing retention of model version history and deployment logs for compliance purposes
Implementing change control processes for AI infrastructure modifications affecting availability
Enforcing segregation of duties in AI system administration and recovery operations
Reporting availability metrics to governance boards with context on AI-specific failure modes
Integrating AI availability risks into enterprise risk management frameworks
Updating policies to address emerging threats to AI system integrity and continuity

Module 9: Cross-Functional Coordination and Incident Response

Establishing incident command roles specific to AI system outages involving model and infrastructure teams
Developing runbooks that include AI model rollback and data revalidation steps
Conducting post-mortems that analyze both technical failures and decision-making during AI incidents
Coordinating communication with downstream systems that consume AI-generated outputs during outages
Training support teams to recognize symptoms of AI model degradation versus infrastructure failure
Integrating AI service status into enterprise-wide incident communication platforms
Managing stakeholder expectations when AI system recovery depends on external data providers
Archiving incident records with model version, configuration, and environmental context for future analysis