Getting an ML model to work in a Jupyter notebook is the easy part. Deploying it as a reliable, cost-effective inference service that handles production traffic? That's where most teams struggle. Here's our playbook for scaling AI inference on AWS.
Instance Selection: The First Decision
The right instance type depends on your model architecture, latency requirements, and budget. For transformer-based models (BERT, GPT), GPU instances (ml.g5.xlarge or ml.g5.2xlarge) are essential. For tree-based models (XGBoost, LightGBM), CPU instances (ml.c5.xlarge) often provide better cost-per-inference.
We benchmark every model across 3-4 instance types before deployment, measuring both P50 and P99 latency under load. The cheapest instance that meets your latency SLA is the right choice - not the fastest one.
Auto-Scaling Strategies
SageMaker supports target-tracking auto-scaling based on invocations per instance. We typically set the target at 70% of the instance's maximum sustainable throughput (determined during load testing). This provides headroom for traffic spikes while keeping costs reasonable.
For workloads with predictable patterns (e.g., business-hours traffic), we combine target tracking with scheduled scaling - pre-warming instances before the morning traffic ramp and scaling down overnight. This reduces cold-start latency spikes and saves 30-40% on compute costs.
Optimization Results (Typical Client)
Model Optimization for Inference
Before deploying, we optimize models for inference speed. For PyTorch models, we use TorchScript compilation and ONNX Runtime for up to 2x speedup. For transformer models, we apply quantization (INT8 or FP16) which typically reduces latency by 40-50% with minimal accuracy loss (<0.5% on most benchmarks).
Batching is another powerful optimization. SageMaker's built-in dynamic batching accumulates requests over a short window (typically 50ms) and processes them as a single batch on the GPU. This can increase throughput by 3-5x for GPU-bound models.
Multi-Model Endpoints
For clients with many models (e.g., per-customer personalization models), SageMaker Multi-Model Endpoints (MME) are a game changer. Instead of deploying each model on its own instance, MME dynamically loads models from S3 on demand. We've seen clients reduce their inference costs by 80% using this approach for long-tail models with sporadic traffic.
Cost Optimization Playbook
- Right-size your instances. Load test first, then pick the cheapest instance that meets your latency SLA.
- Use Savings Plans. For steady-state workloads, 1-year Savings Plans cut costs by 40% vs on-demand pricing.
- Optimize the model, not just the infrastructure. Quantization and compilation often deliver bigger savings than instance upgrades.
- Scale to zero when possible. SageMaker Serverless Inference is ideal for dev/staging environments and low-traffic models.
- Monitor and iterate. Track cost-per-inference and latency percentiles weekly. Small optimizations compound over time.