Phase 6: Cloud Deployment in Client Environments · 55 min · Python · Docker · Kubernetes
Docker & Kubernetes for AI Workloads
Containerize the inference. Orchestrate the scale.
Hiring signal: FDE roles at Databricks, Snowflake, and Scale AI test containerization and K8s knowledge for AI workloads specifically: candidates who can write multi-stage Docker builds, configure GPU scheduling on Kubernetes, and choose between KServe/Seldon/Triton for model serving pass. Candidates who use generic web server patterns for AI inference fail. HPA and KEDA scaling for AI workloads is the production differentiator.
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
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 Multi-Stage Dockerfile for AI Inference, Kubernetes Deployment with GPU Scheduling, HPA with Custom Metrics for AI Workloads, Inference Serving: KServe vs Seldon vs Triton, KEDA: Event-Driven Autoscaling, Practical Application, What Hiring Managers Look For, Resources, Key Takeaways, Next Steps — plus a hands-on lab, quiz, and project artifact.
Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.
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