AI Factory Model Use Cases and Personas v1.3
This guide outlines practical implementation scenarios for AI Factory Models across different organizational personas and use cases. Each scenario provides specific implementation pathways aligned with role-based requirements.
Persona-Based Implementation Scenarios
Platform Engineers
Platform Engineers focus on infrastructure provisioning, system reliability, and operational excellence across AI model deployments.
Infrastructure Management Scenario
Objective: Establish reliable, scalable model serving infrastructure that supports multiple development teams and production workloads.
Key Requirements:
- Multi-tenant resource isolation for different teams and projects
- Automated scaling based on inference demand patterns
- Comprehensive monitoring and alerting for proactive issue resolution
- Security controls that align with organizational policies
Implementation Approach:
- Infrastructure Foundation
- Configure GPU-enabled Kubernetes nodes with appropriate taints and labels
- Implement network policies for secure multi-tenant isolation
- Establish resource quotas and limits aligned with organizational capacity planning
- Operational Framework
- Deploy monitoring stack integration for inference service observability
- Configure automated backup procedures for model metadata and configurations
- Implement disaster recovery procedures for critical model serving workloads
- Governance Integration
- Establish approval workflows for production model deployments
- Configure security scanning pipelines for model image validation
- Implement audit logging for compliance and operational forensics
Reference Documentation:
AI Engineers
AI Engineers require streamlined model deployment workflows that abstract infrastructure complexity while providing comprehensive control over model performance characteristics.
Model Deployment Optimization Scenario
Objective: Deploy and optimize large language models for production inference with minimal operational overhead.
Key Requirements:
- Framework-agnostic deployment procedures that support diverse model types
- Performance optimization capabilities including quantization and batch processing
- A/B testing frameworks for comparing model variants
- Integration with existing AI pipelines and workflows
Implementation Strategy:
- Model Preparation
- Register model images in the Asset Library with comprehensive metadata
- Configure ServingRuntime optimizations for specific model frameworks
- Establish performance baselines through systematic benchmarking
- Deployment Configuration
- Create InferenceServices with resource allocation aligned to model requirements
- Implement auto-scaling policies based on inference demand patterns
- Configure canary deployment strategies for safe production updates
- Performance Monitoring
- Establish model-specific metrics for latency, throughput, and accuracy tracking
- Implement automated performance regression detection
- Configure alerting for model performance degradation
Reference Documentation:
Application Developers
Application Developers integrate model inference capabilities into business applications while maintaining development velocity and operational simplicity.
RAG Application Development Scenario
Objective: Build retrieval-augmented generation applications that leverage private embedding models and language models within organizational infrastructure.
Key Requirements:
- Standardized API interfaces compatible with existing application frameworks
- Consistent model versions across development and production environments
- Low-latency inference for interactive user experiences
- Integration with vector databases and knowledge management systems
Development Workflow:
- Model Integration
- Identify approved embedding and language models through the Model Library
- Configure application clients using standardized OpenAI-compatible endpoints
- Implement error handling and fallback strategies for model availability
- Performance Optimization
- Implement caching strategies for frequently requested embeddings
- Configure batch processing for bulk document embedding operations
- Optimize request patterns to minimize inference latency
- Production Deployment
- Establish environment-specific model endpoint configurations
- Implement monitoring for application-specific model usage patterns
- Configure automated failover between model instances
Reference Documentation:
DevOps Engineers
DevOps Engineers focus on CI/CD integration, deployment automation, and operational reliability for model serving workloads.
Automated Model Deployment Pipeline Scenario
Objective: Implement continuous deployment pipelines that automatically update model serving infrastructure based on model registry changes.
Key Requirements:
- Automated model validation and deployment workflows
- Integration with existing CI/CD infrastructure and approval processes
- Rollback capabilities for failed deployments or performance regressions
- Comprehensive logging and audit trails for deployment activities
Pipeline Implementation:
- Automation Framework
- Configure webhook triggers for model registry updates
- Implement automated validation pipelines for model compatibility testing
- Establish deployment gates based on performance and security criteria
- Deployment Orchestration
- Create infrastructure-as-code templates for InferenceService deployments
- Implement blue-green deployment strategies for zero-downtime updates
- Configure automated rollback procedures based on health check failures
- Monitoring Integration
- Establish deployment success/failure metrics and alerting
- Implement performance comparison dashboards for pre/post deployment analysis
- Configure audit logging for deployment activities and approval workflows
Reference Documentation:
Security Engineers
Security Engineers implement comprehensive security controls for model serving infrastructure while ensuring compliance with organizational and regulatory requirements.
Secure Model Serving Architecture Scenario
Objective: Establish security controls that protect model assets and inference data throughout the model serving lifecycle.
Key Requirements:
- Zero-trust network architecture for model inference traffic
- Comprehensive audit logging for model access and usage patterns
- Vulnerability management for model images and serving infrastructure
- Data privacy controls for inference requests and responses
Security Implementation:
- Access Control Framework
- Implement role-based access control for model registration and deployment operations
- Configure network policies for secure multi-tenant model isolation
- Establish API authentication and authorization for inference endpoints
- Vulnerability Management
- Configure automated security scanning for model images during registration
- Implement vulnerability remediation workflows for identified security issues
- Establish security approval gates for production model deployments
- Audit and Compliance
- Configure comprehensive audit logging for all model operations
- Implement data retention policies aligned with regulatory requirements
- Establish compliance reporting capabilities for security assessments
Reference Documentation:
Cross-Functional Use Cases
Enterprise Sovereign AI Implementation
Scenario: Large enterprise implementing comprehensive AI governance while maintaining operational efficiency across multiple business units.
Stakeholders: Platform Engineers, Security Engineers, Compliance Teams
Implementation Requirements:
- Multi-tenant model serving infrastructure supporting diverse business requirements
- Comprehensive governance framework ensuring regulatory compliance
- Integration with existing enterprise identity and access management systems
- Performance optimization across different model types and usage patterns
Solution Architecture:
- Infrastructure Layer
- Deploy Hybrid Manager across multiple Kubernetes clusters for geographic distribution
- Implement federated model registry with synchronized governance policies
- Configure cross-cluster networking for model sharing and load balancing
- Governance Integration
- Establish enterprise-wide approval workflows with business unit delegation
- Implement automated compliance reporting for regulatory requirements
- Configure security policies aligned with enterprise risk management frameworks
- Operational Excellence
- Deploy comprehensive monitoring across all model serving infrastructure
- Implement automated capacity planning based on historical usage patterns
- Establish incident response procedures for model serving disruptions
Hybrid Cloud Model Deployment
Scenario: Organization deploying models across on-premises and cloud infrastructure while maintaining consistent operational procedures.
Stakeholders: Platform Engineers, DevOps Engineers, Network Engineers
Implementation Considerations:
- Network connectivity and latency optimization between deployment environments
- Consistent security policies across different infrastructure providers
- Data locality requirements for model inference and training workloads
- Disaster recovery capabilities spanning multiple infrastructure tiers
Architecture Components:
- Federated Hybrid Manager deployment across cloud and on-premises infrastructure
- Unified model registry with environment-specific deployment configurations
- Cross-environment monitoring and alerting integration
- Consistent backup and disaster recovery procedures
Implementation Considerations
Resource Planning
Model serving infrastructure requires substantial computational resources, particularly for large language models and high-throughput inference workloads. Organizations should establish capacity planning procedures based on anticipated model sizes and usage patterns.
Resource Allocation Guidelines:
- GPU memory requirements scale directly with model parameter counts
- CPU allocation affects preprocessing and postprocessing performance
- Network bandwidth impacts high-throughput inference scenarios
- Storage performance affects model loading and checkpoint operations
Operational Complexity
Managing diverse model types across different frameworks requires substantial operational expertise. Establish comprehensive training programs and operational procedures before scaling to production workloads.
Operational Best Practices:
- Implement standardized deployment procedures across different model types
- Establish clear escalation procedures for operational issues
- Maintain comprehensive documentation for troubleshooting and maintenance
- Regular operational reviews to identify improvement opportunities
Performance Optimization
Model inference performance depends on multiple factors including hardware configuration, model characteristics, and application usage patterns. Implement systematic performance testing and optimization procedures.
Optimization Strategies:
- Establish performance baselines for all deployed models
- Implement automated performance regression detection
- Configure resource allocation based on actual usage patterns
- Regular performance reviews and optimization cycles
These use cases provide concrete implementation pathways for different organizational roles while highlighting the comprehensive capabilities of AI Factory Models across diverse deployment scenarios.