ml-infrastructure

To set up ML workload autoscaling on Kubernetes, you need deployment manifests that define scaling rules for CPU and GPU resources. This skill generates those manifests, providing deterministic configurations for automatic cluster scaling during peak workloads.

Cost-aware ML infrastructure provisioning balances compute performance with cloud expenses using targeted autoscaling strategies. It ensures resources scale dynamically with workload demands, keeping training and inference pipeline costs predictable without manual resource planning.

Yes, you need existing cloud credentials and Kubernetes cluster access to provision ML infrastructure. The skill validates these prerequisites before applying any deployment manifests, ensuring your environment is ready for secure production-grade setup.

To apply security hardening for production ML pipelines, you configure RBAC and network policies within your Kubernetes cluster. This automation provides the necessary governance controls to secure deployments against unauthorized access and network threats.

Yes, you can deploy mixed training and inference pipelines on the same Kubernetes cluster. The skill designs ML cluster environments with autoscaling configured to handle mixed workloads efficiently across both on-prem and cloud providers.

ML infrastructure deployments require deterministic runbooks to reduce operational toil and prevent configuration errors. Repeatable runbooks ensure cluster setup, security hardening, and autoscaling manifest application produce identical results across multiple environments.