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How to Scale AI Infrastructure With Kubernetes and Docker
- Organizations are increasingly relying on scalable and resilient AI infrastructures to meet cloud requirements.
- Kubernetes and Docker are widely used for scalable AI infrastructures due to their effectiveness.
- Scalability is crucial for handling growing workloads and data volumes in AI systems.
- Kubernetes and Docker ensure optimized performance and effective resource management for AI applications.
- Containerization plays a key role in the evolution of scalable AI infrastructure.
- Defining a Dockerfile and building a Docker image are essential steps in deploying AI models.
- Kubernetes provides orchestration features for efficient application management in containerized infrastructures.
- Horizontal and vertical scaling with Kubernetes allows for maximizing resource utilization and performance.
- Horizontal scaling involves adding replicas to handle higher workloads dynamically.
- Horizontal Pod Autoscaler (HPA) facilitates automatic scaling based on CPU or memory usage in Kubernetes.
- Vertical scaling adjusts resource requests and limits for containers in Kubernetes to provide more resources.
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