Model Deployment Strategies

Learn how to deploy ML models to production — from REST APIs to batch inference, edge deployment, and serving at scale with low latency.

55 min•By Priygop Team•Last updated: Feb 2026

Deployment Patterns

REST API: Serve model predictions via HTTP endpoints — use FastAPI/Flask + model framework. Best for real-time, low-volume predictions (< 1000 QPS)
Batch Inference: Process large datasets on a schedule — run predictions on millions of records nightly. Best for recommendations, risk scoring, ETL pipelines
Streaming: Real-time inference on data streams — use Kafka + model serving. Best for fraud detection, real-time recommendations, IoT
Edge Deployment: Run models on devices (mobile, IoT, browsers) — use TFLite, ONNX Runtime, CoreML. Best for low-latency, offline, and privacy-sensitive applications
Embedded: Model compiled into application code — use ONNX, TorchScript, or PMML. Best for latency-critical applications where network calls are unacceptable
Serverless: Deploy on AWS Lambda, Google Cloud Functions — auto-scales to zero, pay-per-invocation. Best for irregular, low-to-medium traffic

Quantization: Reduce model precision from FP32 to INT8 or FP16 — 2-4x faster inference, 2-4x smaller model size, with minimal accuracy loss (< 1%)
Pruning: Remove unimportant weights (set to zero) — can remove 50-90% of parameters with < 2% accuracy drop. Structured pruning removes entire neurons/filters
Knowledge Distillation: Train a small 'student' model to mimic a large 'teacher' model — DistilBERT is 60% smaller and 60% faster than BERT with 97% of its accuracy
ONNX Export: Convert models from any framework to ONNX format — enables framework-agnostic deployment and hardware-specific optimizations
Model Compilation: Use TorchScript, TF SavedModel, or TensorRT to compile models into optimized inference engines — 2-5x speedup on GPUs
Batching: Group multiple inference requests into a single batch — dramatically improves GPU utilization and throughput