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Introduction to AI Infrastructure

Building robust infrastructure for AI workloads requires specialized knowledge across compute, storage, networking, and orchestration.

What You'll Learn​

  • GPU Cluster Setup — Configure and manage GPU resources
  • Kubernetes for ML — KubeFlow, Ray, Seldon for orchestrating ML workloads
  • Model Serving — TensorRT, vLLM, Triton Inference Server
  • MLOps Pipelines — CI/CD for machine learning
  • Data Engineering — Feature stores and data pipelines for AI
  • Cost Optimization — Managing cloud costs for AI workloads
  • Infrastructure as Code — Terraform and Pulumi for AI environments

More guides coming soon.