AI Model Deployment: From Prototype to Production

Your AI model works in the notebook. Now what? Here’s how to get it production-ready.

The Deployment Challenge

70% of AI projects never reach production. Common blockers:

• Infrastructure complexity
• Performance issues
• Monitoring gaps
• Skill shortages

Deployment Options

1. Cloud AI APIs

Use provider-managed models:

• OpenAI, Anthropic, Google
• No infrastructure to manage
• Pay per use
• Quick start

Best for: Standard use cases, fast deployment

2. Cloud ML Platforms

Deploy custom models on cloud:

• AWS SageMaker
• Google Vertex AI
• Azure ML
• Managed infrastructure, you bring models

Best for: Custom models, enterprise scale

3. Self-Hosted

Run models on your infrastructure:

• Full control
• Data stays local
• Complex to manage

Best for: Privacy requirements, cost optimization at scale

4. Edge Deployment

Run models on devices:

• Mobile apps
• IoT devices
• Offline capability

Best for: Low latency, offline needs

Production Requirements

Performance

Metric	Consideration
Latency	Response time requirements
Throughput	Requests per second
Availability	Uptime SLA
Scalability	Handle demand spikes

Reliability

• Automatic scaling
• Load balancing
• Failover
• Health checks

Security

• Authentication
• Encryption
• Input validation
• Rate limiting

Monitoring

• Request logging
• Performance metrics
• Error tracking
• Cost monitoring

Deployment Architecture

Basic API Pattern

Client
  ↓
Load Balancer
  ↓
API Gateway (auth, rate limiting)
  ↓
Model Service (inference)
  ↓
Logging/Monitoring

With Caching

Client
  ↓
Cache Layer (common queries)
  ↓ (cache miss)
Model Service
  ↓
Response + Cache Update

With Queue

Client
  ↓
API (quick response)
  ↓
Job Queue
  ↓
Worker (model inference)
  ↓
Callback/Webhook

Infrastructure Checklist

Before Deployment

□ Model serialized/containerized
□ API endpoints defined
□ Authentication configured
□ Rate limiting set
□ Logging implemented
□ Error handling complete
□ Health checks ready
□ Documentation written

Deployment

□ Test environment verified
□ Load testing complete
□ Rollback plan ready
□ Monitoring active
□ Alerts configured
□ Runbook documented

Post-Deployment

□ Performance baseline established
□ Error rates normal
□ Cost tracking active
□ Usage patterns monitored
□ User feedback collected

Monitoring Essentials

Key Metrics

Metric	Why It Matters
Latency (p50, p99)	User experience
Error rate	Reliability
Request volume	Capacity planning
Model accuracy	Quality over time
Cost per request	Budget management

Alerting Rules

• Latency > threshold
• Error rate spike
• Unusual request patterns
• Cost anomalies
• Model drift detected

Common Patterns

A/B Testing

Request
  ↓
Router (5% new, 95% current)
  ↓        ↓
Model V2   Model V1
  ↓        ↓
Compare metrics

Shadow Mode

Request
  ↓
Model V1 (serves response)
  ↓
Model V2 (runs silently)
  ↓
Compare (log only)

Canary Release

Deploy new version to 5% of traffic
  ↓
Monitor metrics
  ↓
Gradually increase to 100%
  OR
Rollback if issues

Cost Optimization

Strategies

1. Right-size instances - Don’t over-provision
2. Use spot/preemptible - For batch workloads
3. Implement caching - Avoid redundant inference
4. Batch requests - More efficient processing
5. Model quantization - Smaller, faster models

Cost Monitoring

Track by:

• API endpoint
• Customer/tenant
• Use case
• Time of day

Tools and Platforms

Containerization

Tool	Use
Docker	Container creation
Kubernetes	Orchestration
AWS ECS	Managed containers

ML-Specific

Tool	Use
MLflow	Model management
Seldon	Model serving
BentoML	Model packaging
Ray Serve	Scalable serving

Monitoring

Tool	Use
Prometheus	Metrics
Grafana	Visualization
DataDog	Full stack
Weights & Biases	ML-specific

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Need help deploying your AI models? Our team can help.