Building AI Infrastructure from Scratch

Building robust AI infrastructure is essential for teams moving from experiments to production. This guide covers the essential components and architectural decisions.

Infrastructure Layers

1. Compute Layer

Choose based on workload:

Training: Multi-GPU instances (A100, H100)
Inference: GPU (T4, A10) or CPU for smaller models
Experimentation: Spot instances for cost optimization

2. Data Layer

typescript

// Example: Data pipeline with Bunimport { Database } from "bun:sqlite";const db = new Database("training_data.db");// Store embeddings efficientlydb.run(`  CREATE TABLE IF NOT EXISTS embeddings (    id INTEGER PRIMARY KEY,    text TEXT,    embedding BLOB,    metadata JSON  )`);

3. Model Registry

Track model versions, metadata, and performance:

Model artifacts (weights, configs)
Training metrics and hyperparameters
Evaluation results
Deployment history

Essential Services

Monitoring Stack

yaml

# docker-compose.ymlservices:  prometheus:    image: prom/prometheus    ports: ["9090:9090"]  grafana:    image: grafana/grafana    ports: ["3000:3000"]  node-exporter:    image: prom/node-exporter    ports: ["9100:9100"]

Experiment Tracking

Implement experiment tracking to maintain reproducibility:

Log hyperparameters
Track metrics over time
Store artifacts
Compare runs

Best Practices

Start simple: Don't over-engineer initially
Automate everything: Infrastructure as code
Monitor proactively: Set up alerts early
Plan for scale: Design with growth in mind
Document thoroughly: Future you will thank you

Cost Optimization

AI infrastructure can be expensive. Optimize through:

Spot instances for training
Autoscaling for inference
Efficient model serving (batching, quantization)
Resource monitoring and rightsizing

Conclusion

Building AI infrastructure is an iterative process. Start with core components, measure everything, and evolve based on real usage patterns.