How can I scale my machine learning models using Kubernetes?
An example of an AI chatbot conversation about scaling ML models with Kubernetes.
How can I scale my machine learning models using Kubernetes?
You can scale your ML models in Kubernetes by using features like horizontal pod autoscaling, which adjusts the number of pods based on CPU or memory usage.
What are the benefits of using Kubernetes for ML model deployment?
Kubernetes offers benefits like automated scaling, load balancing, and easy management of containerized applications, making it ideal for ML deployments.
Can you explain how to set up horizontal pod autoscaling?
Sure! You can set up horizontal pod autoscaling by defining a deployment and then creating an HPA resource that specifies the metrics for scaling.
What metrics should I monitor for effective scaling?
Common metrics include CPU utilization, memory usage, and custom metrics like request latency or throughput.
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