Case Study
Simplifying Enterprise AI Development and Deployment with Enterprise Model Vault
Download Case StudyIndustry: Enterprise SaaS
Key Technologies: React, TypeScript, Tailwind CSS, Python, FastAPI, Kubernetes, Azure Kubernetes Service (AKS), Snowflake, Azure Data Lake Storage (ADLS), vLLM, LoRA (Low- Rank Adaptation) Fine-Tuning, WebSockets
About the Client
The client is a leading enterprise software company that provides a cloud-based platform for creating and managing automated business workflows. Its platform helps organizations streamline enterprise processes and manage digital workflows at scale. As part of its continued investment in AI and cloud technologies, the company is advancing its enterprise AI capabilities to support scalable and governed AI development.
Business Challenge
As AI adoption expanded across the enterprise, a leading enterprise technology company found it increasingly difficult to manage AI development across multiple teams. Building, fine-tuning, deploying, and managing AI models required a secure and scalable environment, but the lack of a centralized platform made infrastructure management, governance, and AI Model Lifecycle Management more complex. The organization needed an AI Platform Engineering solution that could standardize AI development while providing secure access to enterprise AI resources.
Key challenges included:
- Fragmented AI Development Processes: AI teams worked independently, so there was no common approach to building or deploying models. Different tools and frameworks became the norm, and teams often repeated similar work.
- Complex GPU Infrastructure Management: Supporting large AI models meant dealing with GPU provisioning, resource scheduling, and day-to-day infrastructure operations. For many teams, this added another layer of complexity beyond model development itself.
- Foundation Model Management: Teams needed a secure, governed process to onboard, evaluate, store, and deploy enterprise-approved foundation models.
- Enterprise Data Integration: AI teams required secure access to enterprise datasets while maintaining governance, compliance, and auditability.
- Limited Visibility into AI Workloads: Training and inference workloads lacked centralized monitoring and real-time operational visibility.
The Solution
Jade Global partnered with the organization to build Enterprise Model Vault, a cloud-native LLMOps Platform that simplifies AI Platform Engineering through standardized model development, deployment, and governance. The platform streamlines AI Model Lifecycle Management by providing secure access to enterprise datasets, automated workload orchestration, scalable inference services, and centralized monitoring through a unified interface.
Features of the Solution
- Self-Service AI Platform: The platform includes a React-based interface where users can browse approved enterprise datasets, configure training jobs, deploy inference services, and monitor workloads in one place.
- Centralized API Orchestration: FastAPI sits at the center of the platform and manages request validation, workflow execution, Kubernetes resource creation, and communication between platform components.
- AI Model Lifecycle Management: Training and inference workloads run through Kubernetes-native TrainingJob and InferenceJob Custom Resource Definitions (CRDs). Kubernetes Operators automate routine lifecycle activities, reducing manual administration.
- LoRA-Based Fine-Tuning: LoRA was adopted as the fine-tuning approach to make model customization more efficient while reducing the compute resources required for training.
- Enterprise Storage Architecture: Stored datasets, model artifacts, adapters, and training outputs centrally in Azure Data Lake Storage (ADLS), giving teams a consistent location to manage AI assets.
- LLMOps Platform for Scalable Inference: Leveraged vLLM-based infrastructure to provide scalable inference services for enterprise AI workloads.
- Real-Time Monitoring: Implemented Kubernetes Watchers and WebSocket-based event streaming to provide live visibility into training and inference workloads.
Key Use Cases Delivered
- Enterprise Model Vault: Brought model onboarding, fine-tuning, deployment, and monitoring together in one centralized platform, giving teams a consistent way to build and manage AI models.
- AI Model Lifecycle Management: Kubernetes-native orchestration handles model training, inference, deployment, and lifecycle operations through automated workflows.
- LLMOps Platform: Supports the deployment and ongoing management of foundation models and inference services for enterprise AI workloads at scale.
- Enterprise AI Data Access: Enterprise datasets are made available through a secure, governed framework for AI development and experimentation.
- Real-Time AI Workload Monitoring: Live status updates provide visibility into training and inference workloads, making it easier to monitor long-running AI jobs.
Business Benefits Achieved
- Standardized AI Development: Teams now use a shared platform for model training, fine-tuning, deployment, and monitoring, replacing the different tools and workflows that were previously used across the organization.
- Simplified Infrastructure Management: The platform handles routine tasks such as GPU provisioning and cloud-native infrastructure management, letting teams spend less time on infrastructure administration.
- Accelerated AI Experimentation: Standardized workflows make it easier to evaluate foundation models and complete fine-tuning activities more efficiently.
- Optimized GPU Utilization: Shared GPU resources can be allocated more effectively across AI training and inference workloads.
- Improved Governance and Security: Enterprise AI workloads continue to operate within a governed environment that supports security, auditability, and machine learning model lifecycle management.
- Simplified AI Operations: A centralized platform brings together deployment, monitoring, and lifecycle operations instead of managing them through separate tools.
- Scalable Foundation for Enterprise AI: The platform is designed to support future AI initiatives as enterprise adoption continues to grow.
| React + TypeScript | User Interface | Self-service user experience |
| FastAPI | API Layer | High-performance orchestration services |
| Kubernetes Operator | Workload Management | Automated scheduling and lifecycle management |
| Custom CRDs | Training & Inference Workflows | Extensible and scalable architecture |
| NVIDIA H100 GPUs | Compute Platform | Supports large-scale AI workloads |
| LoRA | Fine-Tuning | Faster training with reduced infrastructure cost |
| Snowflake | Enterprise Data Source | Secure access to organizational datasets |
| ADLS | Storage Platform | Centralized artifact and dataset management |
| vLLM | Inference Serving | Scalable and efficient model deployment |
| WebSockets | Monitoring | Real-time workload visibility |
Conclusion
By partnering with Jade Global, the organization built Enterprise Model Vault, a cloud-native LLMOps Platform that simplifies AI Platform Engineering and standardizes AI Model Lifecycle Management. The platform provides a scalable foundation for machine learning model lifecycle management, enabling teams to accelerate AI innovation while maintaining governance, operational consistency, and infrastructure efficiency.
