docs: enhance Model Serving Management Guide with comprehensive documentation

[ChenYi015]

Purpose of this PR

This PR significantly enhances the Model Serving Management Guide (docs/serving/index.md) to provide users with a comprehensive reference for deploying, managing, and monitoring inference services using the Arena CLI.

Proposed changes:

• Added an overview section explaining Arena's model serving capabilities
• Documented all supported serving frameworks: TensorFlow Serving, NVIDIA Triton, KServe, KFServing, TensorRT, Custom Serving, Seldon Core, and Distributed Serving
• Included quick start guide with deployment example commands
• Added detailed workflow examples for common patterns (simple serving, multi-version deployment with traffic splitting, GPU serving)
• Added troubleshooting section covering model loading issues, out-of-memory errors, and inference timeouts
• Added "Next Steps" section linking to related guides
• Added "See Also" section with references to CLI, Training, Model Management, and Monitoring guides
• Improved navigation with comprehensive links to all serving-related documentation

Change Category

• Bugfix (non-breaking change which fixes an issue)
• Feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that could affect existing functionality)
• Documentation update

Rationale

The original serving documentation was minimal (~50 lines) and did not provide users with adequate guidance on model serving operations. This enhancement makes the documentation more user-friendly and helps users understand:

• What model serving frameworks Arena supports
• How to deploy models for inference
• How to manage model versions and traffic routing
• How to troubleshoot common serving issues

[google-oss-prow]

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