Ultralytics YOLO in Production

Take a trained Ultralytics YOLO model out of the lab and into production — runtimes, optimization, tracking, multi-stream inference, and the observability you need to keep it healthy.

By Ultralytics Academy

Begin course Sign in to save progress

What you'll learn

Pick a runtime, optimize the model for it, run multiple streams in parallel, build practical pipelines (counting, heatmaps), and observe the system in production.

Pick a deployment target and the matching runtime — ONNX Runtime, TensorRT, OpenVINO, CoreML, QNN.
Optimize for latency with FP16, INT8, and dynamic batch sizing.
Track objects across frames with ByteTrack or BoT-SORT and use IDs in pipelines.
Build counting, heatmap, and speed-estimation solutions on top of detection.
Run multiple camera streams concurrently without dropping frames.
Observe accuracy, latency, and drift in production.

What you'll build

An optimized engine (TensorRT or OpenVINO) for your target hardware with verified parity to the .pt model.
A counting pipeline with line crossing or zone occupancy on top of tracking.
A multi-stream service that processes 4+ camera feeds concurrently.
A monitoring dashboard wired to drift / latency / detection volume metrics.

Prerequisites

A trained model (or a pretrained Ultralytics YOLO checkpoint to follow along).
Comfort with Python, the command line, and basic networking (ports, RTSP).
Recommended: complete Building High-Performance YOLO Datasets and Train your first YOLO model first.

Course content

4 modules · 9 lessons

Module 1

Choose a Runtime

Choose a Deployment Target

Map your hardware and constraints to the runtime that wins on it.

Export to ONNX (and Verify Parity)

The portable starting point — and how to confirm the export didn't quietly break the model.

Module 2

Optimize

Optimize with TensorRT

FP16 and INT8 — when each one wins, and what to test before you ship.

OpenVINO on CPU

Real CPU speedups via Intel's optimized kernels — useful when GPUs aren't an option.

Module 3

Track and Build Pipelines

Object Tracking with ByteTrack and BoT-SORT

Persistent IDs across frames — the foundation of every counting, alerting, and analytics pipeline.

Counting, Heatmaps, and Speed Estimation

Three of the most common downstream pipelines — and the geometry behind them.

Module 4

Scale and Observe

Multi-Stream Inference

Run multiple cameras concurrently without dropping frames.

Observability and Drift

What to log, what to dashboard, and how to spot accuracy regression before users do.

Cost and Latency Tuning

The handful of knobs that move the needle, and the ones that don't.