ml-infrastructure

What problem does it solve?

Production ML infrastructure provisioning and management is complex and time-consuming; this skill automates the setup, scaling, and governance of ML infra to reduce toil and errors.

Core Features & Use Cases

• Kubernetes-based ML cluster design and deployment with auto-scaling for CPU and GPU workloads.
• Cost-aware resource planning and autoscaling strategies to balance performance and expense.
• Security hardening, RBAC, and network policies for secure production ML pipelines.
• Use Case: Deploy a training and inference pipeline that scales automatically during peak workloads while keeping costs predictable.

Quick Start

Use the ml-infrastructure skill to provision a production ML environment and configure auto-scaling for a mixed workload.