🎯 Model Serving Strategies
Choose the right serving strategy for your ML workload
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API Design for ML Models
Introduction to Model Serving
🎯 What is Model Serving?
Model serving is the process of making ML models available for inference in production. The choice of serving strategy depends on latency requirements, throughput needs, cost constraints, and deployment environment. Different workloads demand different approaches.
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Key Insight
No single serving strategy fits all use cases. Match the strategy to your requirements.
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Online Serving
Real-time predictions with low latency
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Batch Serving
High-throughput bulk processing
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Edge Serving
On-device inference without network
🔍 Choosing a Strategy
1
Latency Requirements
How fast must predictions return?
2
Volume & Throughput
How many predictions per second?
3
Cost Constraints
Budget for infrastructure and compute?
4
Deployment Environment
Cloud, edge device, or hybrid?
✅ Consider
- •User experience needs
- •Data freshness requirements
- •Scalability projections
- •Privacy constraints
⚠️ Trade-offs
- •Latency vs throughput
- •Cost vs performance
- •Complexity vs flexibility
- •Freshness vs efficiency