End-to-End LLMOps Implementation
Client: Enterprise AI Platform (Ishtar AI Case Study)
The Challenge
Building a complete, production-ready LLMOps platform requires integrating infrastructure, CI/CD, monitoring, scaling, and governance into a cohesive system. Each component must work together seamlessly to deliver reliable, auditable, and scalable LLM applications.
Our Solution
We implemented a comprehensive end-to-end LLMOps platform that integrates all components: Infrastructure-as-Code foundation, continuous evaluation pipelines, advanced observability, scalable serving infrastructure, and governance frameworks. The platform provides a complete operational contract for LLM applications.
Results
Delivered a production-ready LLMOps platform that supports multiple LLM applications with 99.9% uptime, comprehensive audit trails, and rapid iteration capabilities. Enabled deployment of new LLM applications in days rather than weeks.
Measurement Period: 12 months post-deployment
Methodology: Platform metrics tracking and operational analysis
Time-to-Value
Total Duration: 13 weeks
- Kickoff: Week 1
- Architecture Review:
- Build Complete:
- Pilot Deployment:
- Production Rollout: Week 13
Architecture & Scope
Components Deployed
- Infrastructure-as-Code foundation
- Kubernetes orchestration
- Continuous evaluation pipelines
- Advanced observability stack
- Scalable serving infrastructure
- Governance and compliance framework
- Multi-agent system support
Integration Points
- Cloud infrastructure
- CI/CD systems
- Monitoring and alerting
- All LLM applications
- Compliance systems
Risk & Controls Implemented
Audit Trails
End-to-end traceability across all components
Permission Models
Comprehensive RBAC across platform
Evaluation Harnesses
Continuous evaluation and quality gates
Compliance Controls
Complete compliance framework with auditability
Artifacts
Screenshots
Sample Outputs
Complete platform architecture and operational runbooks
Advanced Large Language Model Operations
Springer Nature, March 2026
Chapter: Chapter 12: Case Study Conclusion - Implementing Ishtar AI End-to-End
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