AI Platform Engineering¶
Summary
AI Platform Engineering is the discipline of designing, building, and operating the infrastructure stack that takes machine learning models from experiments to reliable production services. Unlike traditional platform engineering (CPU-centric, database-backed), AI platform engineering centers on GPU compute — the most expensive and constrained resource in the stack. It extends Kubernetes with specialized schedulers, device plugins, distributed compute frameworks, and inference-optimized serving engines to maximize GPU utilization while maintaining performance SLAs.
Why AI Workloads Are Different¶
Traditional applications follow a well-understood pattern: User → API → Database. The bottleneck is typically CPU, memory, or database performance, and horizontal scaling is straightforward.
AI inference workloads follow a fundamentally different pattern: User → Inference Server → GPU → Model Weights. The bottleneck shifts from data serving to compute serving:
| Dimension | Traditional Applications | AI Applications |
|---|---|---|
| Primary resource | CPU, memory | GPU memory, GPU compute |
| Bottleneck | Database I/O, network latency | Model loading time, memory bandwidth |
| Idle cost | Idle CPU is tolerable | Idle GPU is extremely expensive |
| Scaling | Horizontal (add replicas) | GPU-aware (multi-GPU coordination) |
| Scheduling | Place anywhere with capacity | Topology-aware, gang scheduling |
| Serving | Stateless request/response | Stateful KV cache, batched inference |
Core Problem Statement¶
The central challenge is GPU utilization. Organizations invest thousands of dollars in GPU hardware to accelerate model execution. If those GPUs sit idle or are underutilized due to resource fragmentation, infrastructure costs increase without delivering value.
Platform engineers must answer:
- How do we keep GPUs busy?
- How do we schedule workloads without wasting compute?
- How do we serve more inference requests per GPU?
- How do we orchestrate distributed training across multiple GPUs and nodes?
Why Kubernetes Alone Is Not Enough¶
Kubernetes excels at scheduling containers against CPU and memory resources. AI workloads introduce requirements that standard Kubernetes cannot handle natively:
- GPU-aware scheduling — GPUs must be discovered and registered via device plugins before Kubernetes can manage them
- Gang scheduling — distributed training jobs require all GPUs allocated simultaneously or not at all
- Topology awareness — GPU placement across nodes affects inter-GPU communication latency
- Multi-GPU coordination — training and inference may span multiple GPUs on a single node or across nodes
- Resource sharing — time-slicing and MIG partitioning for efficient sub-GPU allocation
- Distributed compute — task and actor scheduling above the infrastructure layer (Ray)
- Inference optimization — KV cache management, continuous batching, PagedAttention (vLLM)
This is why the AI infrastructure ecosystem has developed tools like Kubeflow, KServe, Ray, vLLM, Volcano, and Kueue.
The Three-Layer AI Platform Architecture¶
A production AI platform consists of three distinct layers:
Layer 1: AI Applications (Business Layer)¶
The products users interact with:
- Virtual assistants and chatbots
- Recommendation systems
- Fraud detection pipelines
- Content generation services
- IoT analytics
Layer 2: AI Platform Services (MLOps Layer)¶
The platform that takes raw data and turns it into production models:
| Stage | Purpose | Example Tools |
|---|---|---|
| Data Processing | Collect, clean, label, store data | Spark, Flink, Label Studio |
| Feature Engineering | Transform raw data into model features | Feature Stores (Feast, Tecton) |
| Model Training | Distributed GPU training | Kubeflow Training Operator, Ray Train |
| Model Registry | Version, store, track models | MLflow, Weights & Biases |
| Deployment & Inference | Serve predictions reliably | KServe, Ray Serve, vLLM |
| Monitoring | Track latency, drift, costs | Prometheus, Grafana, Evidently |
Layer 3: Infrastructure (Compute Layer)¶
The foundation everything depends on:
| Category | Components |
|---|---|
| Compute | CPUs, GPUs, TPUs |
| Storage | Object storage, data lakes, feature stores |
| Orchestration | Kubernetes, networking, scheduling |
| Accelerators | NVIDIA GPUs (A100, H100), Google TPUs |
Tool Landscape¶
| Layer | Tool | Purpose |
|---|---|---|
| Training Pipelines | Kubeflow, Argo Workflows | End-to-end ML workflow orchestration |
| Model Registry | MLflow | Model versioning, tracking, and metadata |
| Distributed Compute | Ray | Task and actor scheduling across GPU clusters |
| Model Serving | KServe, vLLM | Production inference with GPU optimization |
| Batch Scheduling | Volcano, Kueue | Gang scheduling, queue management, fair-share |
| GPU Management | NVIDIA GPU Operator | Driver lifecycle, device plugins, monitoring |
| Infrastructure | Kubernetes | Container orchestration and resource management |
Evaluation¶
| Dimension | Assessment |
|---|---|
| Maturity | Growing — CNCF ecosystem extending rapidly for AI workloads |
| Complexity | High — multi-layer stack with many moving parts |
| Cost Sensitivity | Critical — GPU costs dominate infrastructure budgets |
| Ecosystem | Active — Kubernetes, Ray, vLLM, Kubeflow all under active development |
| Entry Barrier | Moderate — requires Kubernetes expertise plus GPU/ML domain knowledge |
| Pros | Cons |
|---|---|
| Leverages existing Kubernetes skills | Multi-layer complexity increases operational burden |
| Modular — components can be adopted incrementally | GPU hardware costs remain significant |
| Active open-source ecosystem (CNCF, Ray, vLLM) | Fast-moving ecosystem means frequent breaking changes |
| Enables GPU sharing and cost optimization | Requires specialized knowledge (GPU topology, distributed training) |
| Supports both training and inference workloads | Monitoring and observability tooling still maturing |
Related Topics¶
- LLM Fundamentals — model architecture, quantization, serving engines
- Kubernetes — container orchestration fundamentals
- Docker — container runtime
Sources¶
- 7 Days of AI Platform Engineering — Milind Dethe — primary source for this research
- Day 1: Why AI Workloads Are Different
- Day 2: GPU Discovery in Kubernetes
- Day 3: GPU Utilization & Resource Fragmentation
- Day 4: AI Workload Scheduling
- Day 5: Ray — Distributed Compute
- Day 6: vLLM — Efficient LLM Serving
- Day 7: The Full AI Platform Stack
- Kubernetes Device Plugins — official documentation on extending K8s for hardware accelerators
- NVIDIA MIG User Guide — Multi-Instance GPU partitioning
- Run:ai GPU Time-Slicing — advanced GPU time-slicing modes
- Ray Cluster Key Concepts — head node, worker nodes, autoscaling
- Ray on Kubernetes — KubeRay deployment patterns
- vLLM — high-throughput LLM serving engine
- PagedAttention Paper (arXiv:2309.06180) — memory-efficient attention for LLM serving
- KV Caching Explained — Hugging Face — visual guide to KV cache mechanics
Questions¶
- How will disaggregated prefill/decode architectures (NVIDIA Dynamo, llm-d) change the platform layer?
- What is the practical cost comparison between MIG partitioning vs time-slicing vs dedicated GPU allocation for inference workloads?
- How do Volcano and Kueue compare for production gang scheduling, and when should each be used?
- Will Ray become the default distributed compute layer for all AI platforms, or will Kubernetes-native solutions catch up?
- How should platform teams approach GPU capacity planning when model sizes and inference patterns change rapidly?
- What monitoring signals beyond standard latency/throughput are essential for GPU-intensive workloads (GPU memory fragmentation, SM utilization, NVLink bandwidth)?