multi-tenancy in training

**Multi-tenancy in training** is the **shared-cluster operating model where multiple users or teams run workloads on common infrastructure** - it improves fleet utilization but requires strong isolation, fairness, and performance governance. **What Is Multi-tenancy in training?** - **Definition**: Concurrent workload hosting for many tenants on one training platform. - **Primary Risks**: Noisy-neighbor interference, quota disputes, and policy-driven resource contention. - **Isolation Layers**: Namespace controls, resource limits, network segmentation, and identity enforcement. - **Success Criteria**: Fair access, predictable performance, and secure tenant separation. **Why Multi-tenancy in training Matters** - **Utilization**: Shared infrastructure avoids idle dedicated clusters and improves capital efficiency. - **Access Scalability**: Supports many teams without separate hardware silos for each project. - **Cost Sharing**: Platform overhead is amortized across broader user populations. - **Governance Need**: Without controls, aggressive workloads can starve critical jobs. - **Security Importance**: Tenant boundaries are essential for sensitive data and model assets. **How It Is Used in Practice** - **Policy Framework**: Implement quotas, priorities, and fair-share mechanisms per tenant. - **Isolation Controls**: Use strict RBAC, network policy, and workload sandboxing where required. - **Performance Monitoring**: Track per-tenant usage and interference signals to tune scheduler policy. Multi-tenancy in training is **the operating foundation for shared AI platforms** - success requires balancing utilization efficiency with strict fairness, performance, and security controls.