Merge pull request #58 from oracle-quickstart/vkammari/queue-doc-update

Updated the Teams doc title to Job Queueing and adding a link to this doc from the home readme.

Author: vishnukam3

README.md

@@ -52,7 +52,7 @@ After you install OCI AI Blueprints to an OKE cluster in your tenancy, you can d
 | [**Multi-node Inference with RDMA and vLLM**](./docs/multi_node_inference)            | Deploy Llama-405B sized LLMs across multiple nodes with RDMA using H100 nodes with vLLM and LeaderWorkerSet.                                          |
 | [**Scaled Inference with vLLM**](./docs/auto_scaling)            | Serve LLMs with auto-scaling using KEDA, which scales to multiple GPUs and nodes using application metrics like inference latency.|
 | [**LLM Inference with MIG**](./docs/mig_multi_instance_gpu)            | Deploy LLMs to a fraction of a GPU with Nvidia’s multi-instance GPUs and serve them with vLLM.                                                 |
-| [**Health Check**](./docs/sample_blueprints/gpu-health-check)            | Comprehensive evaluation of GPU performance to ensure optimal hardware readiness before initiating any intensive computational workload.                 |
+| [**Job Queuing**](./docs/sample_blueprints/teams)            | Take advantage of job queuing and enforce resource quotas and fair sharing between teams.                 |
 
 ## Support & Contact
 

docs/sample_blueprints/teams/README.md

@@ -1,6 +1,7 @@
-# Teams
+# Job Queuing
+**Job Queuing** feature in OCI AI Blueprints leverage Kueue, a Kubernetes job queuing system, to manage AI/ML workloads more efficiently. Job Queueing introduces workload queuing, prioritization, and resource-aware scheduling, ensuring that jobs are admitted based on available quotas and resource policies. This features enables fair sharing, dynamic resource allocation, and improved utilization of GPUs across workloads.
 
-**Teams** feature in OCI AI Blueprints lets admins enforce resource quotas and fair sharing between teams to decide when and where a job (batch, HPC, and AI/ML workloads) should wait or run within the cluster.
+**Teams** in OCI AI Blueprints lets admins enforce resource quotas and fair sharing between teams to decide when and where a job (batch, HPC, and AI/ML workloads) should wait or run within the cluster.
 
 Each bucket (a _team_) has hard _nominal quotas_, soft _borrowing_ / _lending_ limits, an optional _priority threshold_, and a friendly name you reference in any job blueprint.  
 Behind the scenes, the blueprint engine uses Kueue and wires up a `ClusterQueue`, `LocalQueue`, and a `Cohort` so workloads from different teams share idle capacity fairly while respecting their quotas. ([Kueue Docs](https://kueue.sigs.k8s.io/docs/overview/))