Description
In the rapidly evolving landscape of enterprise AI, deploying machine learning models at scale presents unique challenges that extend beyond traditional application deployment. Kubernetes has emerged as the de facto platform for orchestrating containerized applications, but deploying AI models introduces additional complexity around resource management, scaling, and model serving.
The intersection of AI/ML workloads and container orchestration requires careful consideration of factors like GPU utilization, model versioning, inference latency, and high availability. Here are the essential components and best practices for successfully deploying AI models in Kubernetes clusters, ensuring production-grade reliability and performance.
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