Phase 6: Cloud Deployment in Client Environments · 55 min · Python · Docker · Kubernetes
Docker & Kubernetes for AI Workloads
Containerize the inference. Orchestrate the scale.
What you will learn
- Write Dockerfiles for AI: multi-stage builds, layer caching, image size optimization
- Deploy on Kubernetes: Deployments, StatefulSets, Jobs for batch processing
- Schedule GPUs on Kubernetes: GPU node pools, resource requests/limits, nvidia.com/gpu
- Choose inference serving: KServe, Seldon Core, Triton Inference Server
- Configure scaling: HPA for request-based, KEDA for event-driven, custom metrics for AI
Objective
What you'll learn
This lesson takes approximately 55 min. By the end, you will be able to:
- Write Dockerfiles for AI: multi-stage builds, layer caching, image size optimization
- Deploy on Kubernetes: Deployments, StatefulSets, Jobs for batch processing
- Schedule GPUs on Kubernetes: GPU node pools, resource requests/limits, nvidia.com/gpu
- Choose inference serving: KServe, Seldon Core, Triton Inference Server
- Configure scaling: HPA for request-based, KEDA for event-driven, custom metrics for AI
The Problem
This lesson covers Docker & Kubernetes for AI Workloads. The challenge in enterprise AI deployments is that the gap between theory and practice is enormous — what works in a tutorial fails inside a real client environment with legacy systems, security constraints, and organizational complexity. This lesson gives you the practical knowledge and hands-on patterns that FDEs use to bridge that gap.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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