Masood openly admits that Kubernetes is overkill for small teams or batch scoring. He provides a decision matrix to help you decide if you even need K8s for your ML workload.
MLflow logs every parameter, code version, metric, and artifact generated during development. This absolute logging ensures that any top-performing model can be reproduced by separate engineering teams. faisal masood machine learning on kubernetes
Masood doesn’t assume you’re a K8s expert. He explains Volumes for dataset storage, Services/Ingress for model APIs, ConfigMaps/Secrets for credentials, and Resource Limits for GPU workloads. Each concept is tied directly to an ML use case. Masood openly admits that Kubernetes is overkill for