ML Infrastructure

Overview

ML Infrastructure encompasses the hardware, software, and services needed to support the entire ML lifecycle — from data processing and model training to deployment and serving at scale.

Infrastructure Components

graph TB
    subgraph "Compute"
        CPU[CPU Clusters]
        GPU[GPU Clusters]
        TPU[TPU Pods]
    end
    
    subgraph "Storage"
        DS[Data Storage<br/>S3, GCS, HDFS]
        AS[Artifact Storage<br/>Models, Features]
        CS[Config Storage<br/>Parameters, Metadata]
    end
    
    subgraph "Orchestration"
        K8[Kubernetes]
        DO[Docker]
        HM[Helm]
    end
    
    subgraph "Serving"
        REST[REST APIs]
        GRPC[gRPC]
        BATCH[Batch Processing]
    end
    
    CPU --> K8
    GPU --> K8
    K8 --> REST
    K8 --> GRPC
    K8 --> BATCH

Compute Infrastructure

GPU vs CPU

Cloud vs On-Premise

graph LR
    subgraph "Cloud"
        A[AWS] --> G1[SageMaker, EC2, EKS]
        B[GCP] --> G2[Vertex AI, GKE]
        C[Azure] -> G3[AML, AKS]
    end
    
    subgraph "On-Premise"
        D[Own Hardware] --> G4[Full Control]
    end
    
    G1 --> P[Pros: Scalable, Managed]
    G4 --> C2[Pros: Data Privacy, Cost Long-term]

Containerization

Docker for ML

FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY model/ ./model/
COPY src/ ./src/

EXPOSE 8080
CMD ["python", "serve.py"]

Kubernetes for ML

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ml-model
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ml-model
  template:
    spec:
      containers:
      - name: model
        image: ml-model:v1.0
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: "4Gi"

Scaling Strategies

graph TB
    subgraph "Horizontal Scaling"
        H1[Add more pods/instances]
        H2[Load balancer distributes traffic]
    end
    
    subgraph "Vertical Scaling"
        V1[Increase resources per instance]
        V2[More CPU/GPU/Memory]
    end
    
    subgraph "Auto-scaling"
        A1[HPA: Horizontal Pod Autoscaler]
        A2[Based on CPU, memory, custom metrics]
    end

Cost Optimization

Interview Questions

1. How would you design ML infrastructure for a startup?
2. What are the trade-offs between cloud and on-premise ML infrastructure?
3. How do you handle GPU resource management?
4. Explain how you’d set up auto-scaling for model serving.
5. How do you optimize ML infrastructure costs?

Common Mistakes

• Over-provisioning: Paying for idle GPU resources
• No monitoring: Can’t optimize what you don’t measure
• Tight coupling: Infrastructure too specific to one framework
• Ignoring networking: Data transfer costs and latency between services

Summary

ML Infrastructure requires careful planning across compute, storage, orchestration, and serving. Cloud providers offer managed solutions (SageMaker, Vertex AI), while Kubernetes provides flexibility. Key considerations include cost optimization, auto-scaling, and choosing the right compute (CPU vs GPU) for each workload.

Cross-References

• Cloud Overview
• Kubernetes
• GPU in Cloud
• MLOps Platforms
• Storage Overview