Meet YOLO26: next-gen vision AI.
Practitioner pathwayintermediate ~4 hours 10 lessons Final exam · Certificate

End-to-end CV — from raw frames to a deployed, monitored model

Build with Ultralytics Platform

Use Ultralytics Platform to source data, auto-annotate, curate, train in the cloud or locally, deploy, and monitor — all without leaving the browser.

By Ultralytics Academy

Begin course Sign in to save progress
Ultralytics ecosystem and Platform integrations
What you'll learn
Run a full CV project on Ultralytics Platform — collect data, label it, train, evaluate, deploy, and observe — and decide which steps to keep on Platform vs your own infrastructure.

  • What Ultralytics Platform is and the problem it actually solves end-to-end.

  • Upload images, videos, and dataset archives and shape them into a Platform dataset.

  • Use SAM-powered smart annotation as a labeling accelerator — with the review loop that catches its mistakes.

  • Curate, version, and split a dataset cleanly inside Platform.

  • Train on cloud GPUs or stream local runs, compare experiments, and pick the right model.

  • Deploy to a dedicated endpoint and monitor latency and request volume in production.

  • Plan around data residency, regions, and compliance — what dataRegion actually means.

What you'll build

  • A dataset sourced and curated entirely on Ultralytics Platform.

  • A trained model with logged experiments, validation artifacts, and a chosen best.pt.

  • A deployed endpoint with monitoring wired up.

  • A runbook for retraining and redeployment when drift is detected.

Prerequisites

  • An Ultralytics Platform account (free tier works for most of the course).

  • Familiarity with Ultralytics YOLO concepts — recommended: complete Computer Vision Foundations, Building High-Performance YOLO Datasets, and Train your first YOLO model first.

  • A small dataset or willingness to use a starter dataset.

Course content

4 modules · 10 lessons

Module 1

Why Platform
1

Why Ultralytics Platform

What Platform is, what it isn't, and the lifecycle it covers.

Module 2

Build the Dataset
2

Upload and Prepare Data

Upload images, videos, and archives — and let Platform dedup and frame-extract for you.

3

Smart Annotation as an Accelerator

Let SAM (or your fine-tuned YOLO) propose annotations, let humans review — and the rules that keep this from poisoning your dataset.

4

Curate, Dedup, and Split

From a labeled pile to a clean train/val/test ready for training.

Module 3

Train and Evaluate
5

Cloud Training

One click, one model size, one GPU class — and a trained model when you come back.

6

Track Experiments

Compare runs, name them well, and keep the lessons.

Module 4

Deploy and Monitor
7

Deploy a Model

From best.pt to a managed inference endpoint with one click.

8

Monitor in Production

Watch latency, detection volume, and drift — and know when to retrain.

9

Privacy, Regions, and Compliance

Where your data lives, where compute happens, and what dataRegion means in practice.

10

From Prototype to Pipeline

The repeating shape of a CV team's quarter — and the artifacts that make it sustainable.