⚡ Scaling Inference
Scale ML models to handle millions of requests efficiently
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Model Versioning
Introduction to Inference Scaling
🎯 The Challenge
Production ML models must handle requests ranging from hundreds to millions per day. As traffic grows, inference becomes the bottleneck. Efficient scaling is critical for user experience and cost management.
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Key Insight
Scaling inference is about maximizing throughput while minimizing latency and cost.
⏱️
Latency
Response time per request
Target: <100ms
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Throughput
Requests processed per second
Target: 1000+ req/s
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Cost
Infrastructure expenses
Optimize GPU use
📈 Scaling Dimensions
🔼Vertical Scaling
Increase resources (CPU/GPU/RAM) on a single machine
✓Simple to implement
✗Hardware limits
↔️Horizontal Scaling
Add more machines/instances to distribute load
✓Unlimited scaling
✗More complexity
🎯 Performance Metrics
P95 Latency<100ms
GPU Utilization>70%
Error Rate<0.1%