Ultralytics Platform

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Ultralytics Platform is a comprehensive end-to-end computer vision platform that streamlines the entire ML workflow from data preparation to model deployment. Built for teams and individuals who need production-ready computer vision solutions without the infrastructure complexity.

What is Ultralytics Platform?

Ultralytics Platform is designed to replace fragmented ML tooling with a unified solution. It combines the capabilities of:

• Roboflow - Data management and annotation
• Weights & Biases - Experiment tracking
• SageMaker - Cloud training
• HuggingFace - Model deployment
• Arize - Monitoring

All in one platform with native support for YOLO26 and YOLO11 models.

Workflow: Upload → Annotate → Train → Export → Deploy

The Platform provides an end-to-end workflow:

graph LR
    subgraph Data["📁 Data"]
        A[Upload] --> B[Annotate]
        B --> C[Analyze]
    end
    subgraph Train["🚀 Train"]
        D[Configure] --> E[Train on GPU]
        E --> F[View Metrics]
    end
    subgraph Deploy["🌐 Deploy"]
        G[Export] --> H[Deploy Endpoint]
        H --> I[Monitor]
    end
    Data --> Train --> Deploy

What you can do:

• Upload images, videos, and ZIP archives to create training datasets
• Visualize annotations with interactive overlays for all 5 YOLO task types (see supported tasks)
• Train models on 22 cloud GPU types with real-time metrics
• Export to 17 deployment formats (ONNX, TensorRT, CoreML, TFLite, etc.)
• Deploy to 43 global regions with one-click dedicated endpoints
• Monitor training progress, deployment health, and usage metrics
• Collaborate by making projects and datasets public for the community

Multi-Region Infrastructure

Your data stays in your region. Ultralytics Platform operates infrastructure in three global regions:

You select your region during onboarding, and all your data, models, and deployments remain in that region.

Key Features

Data Preparation

• Dataset Management: Upload images, videos, or ZIP archives with automatic processing
• Annotation Editor: Manual annotation for all 5 YOLO task types (detect, segment, pose, OBB, classify; see supported tasks)
• SAM Smart Annotation: Click-based intelligent annotation using Segment Anything Model
• Auto-Annotation: Use trained models to pre-label new data
• Statistics: Class distribution, location heatmaps, and dimension analysis

graph LR
    A[Upload ZIP/Images/Video] --> B[Auto-Process]
    B --> C[Browse & Filter]
    C --> D{Annotate}
    D --> E[Manual Tools]
    D --> F[SAM Smart]
    D --> G[YOLO Auto-Label]
    E --> H[Train-Ready Dataset]
    F --> H
    G --> H

Model Training

• Cloud Training: Train on 22 cloud GPU types with real-time metrics
• Remote Training: Train anywhere and stream metrics to the platform (W&B-style)
• Project Organization: Group related models, compare experiments, track activity
• 17 Export Formats: ONNX, TensorRT, CoreML, TFLite, and more (see supported formats)

You can train models either through the web UI (cloud training) or from your own machine (remote training):

# Install ultralytics
pip install "ultralytics>=8.4.14"

# Set your API key
export ULTRALYTICS_API_KEY="your_api_key"

# Train and stream metrics to the platform
yolo train model=yolo26n.pt data=coco.yaml epochs=100 project=username/my-project name=exp1

import os

from ultralytics import YOLO

os.environ["ULTRALYTICS_API_KEY"] = "your_api_key"

model = YOLO("yolo26n.pt")
model.train(
    data="coco.yaml",
    epochs=100,
    project="username/my-project",
    name="exp1",
)
# Metrics stream to Platform automatically

Deployment

• Inference Testing: Test models directly in the browser with custom images
• Dedicated Endpoints: Deploy to 43 global regions with auto-scaling
• Monitoring: Real-time metrics, request logs, and performance dashboards

graph LR
    A[Trained Model] --> B{Action}
    B --> C[Browser Predict]
    B --> D[Export Format]
    B --> E[Deploy Endpoint]
    D --> F[ONNX / TensorRT / CoreML / TFLite / ...]
    E --> G[43 Global Regions]
    G --> H[API Endpoint URL]
    H --> I[Monitor & Scale]

Once deployed, call your endpoint from any language:

import requests

url = "https://your-endpoint-url/predict"
headers = {"Authorization": "Bearer your_api_key"}

with open("image.jpg", "rb") as f:
    response = requests.post(url, headers=headers, files={"file": f})

print(response.json())

curl -X POST "https://your-endpoint-url/predict" \
  -H "Authorization: Bearer your_api_key" \
  -F "file=@image.jpg"

const form = new FormData();
form.append("file", fileInput.files[0]);

const response = await fetch("https://your-endpoint-url/predict", {
    method: "POST",
    headers: { Authorization: "Bearer your_api_key" },
    body: form,
});

const results = await response.json();
console.log(results);

Account Management

• Teams & Organizations: Collaborate with team members, manage roles and invites
• API Keys: Secure key management for remote training and API access
• Credits & Billing: Pay-as-you-go training with transparent pricing
• Activity Feed: Track all account events and actions
• Trash & Restore: 30-day soft delete with item recovery
• GDPR Compliance: Data export and account deletion

Quick Links

Get started with these resources:

• Quickstart: Create your first project and train a model in minutes
• Datasets: Upload and manage your training data
• Annotation: Label your data with manual and AI-assisted tools
• Projects: Organize your models and experiments
• Cloud Training: Train on cloud GPUs
• Inference: Test your models
• Endpoints: Deploy models to production
• Monitoring: Track deployment performance
• API Keys: Manage API access
• Billing: Credits and payment
• Activity: Track account events
• Trash: Recover deleted items
• REST API: API reference

FAQ

How do I get started with Ultralytics Platform?

To get started with Ultralytics Platform:

1. Sign Up: Create an account at platform.ultralytics.com
2. Select Region: Choose your data region (US, EU, or AP) during onboarding
3. Upload Dataset: Navigate to the Datasets section to upload your data
4. Train Model: Create a project and start training on cloud GPUs
5. Deploy: Test your model and deploy to a dedicated endpoint

For a detailed guide, see the Quickstart page.

What are the benefits of Ultralytics Platform?

Ultralytics Platform offers:

• Unified Workflow: Data, training, and deployment in one place
• Multi-Region: Data residency in US, EU, or AP regions
• No-Code Training: Train advanced YOLO models without writing code
• Real-Time Metrics: Stream training progress and monitor deployments
• 43 Deploy Regions: Deploy models close to your users worldwide
• 5 Task Types: Support for detection, segmentation, pose, OBB, and classification (see task docs)
• AI-Assisted Annotation: SAM and auto-labeling to speed up data preparation

What GPU options are available for cloud training?

Ultralytics Platform supports multiple GPU types for cloud training:

See Cloud Training for complete pricing and GPU options.

How does remote training work?

You can train models on your own hardware and stream real-time metrics to the platform, similar to Weights & Biases.

pip install "ultralytics>=8.4.14"

# Set your API key
export ULTRALYTICS_API_KEY="your_api_key"

# Train with project/name to stream metrics
yolo train model=yolo26n.pt data=coco.yaml epochs=100 project=username/my-project name=exp1

import os

from ultralytics import YOLO

os.environ["ULTRALYTICS_API_KEY"] = "your_api_key"

model = YOLO("yolo26n.pt")
model.train(
    data="coco.yaml",
    epochs=100,
    project="username/my-project",
    name="exp1",
)

# Train using a Platform dataset directly
export ULTRALYTICS_API_KEY="your_api_key"

yolo train model=yolo26n.pt data=ul://username/datasets/my-dataset epochs=100 project=username/my-project name=exp1

See Cloud Training for more details on remote training.

What annotation tools are available?

The Platform includes a full-featured annotation editor supporting:

• Manual Tools: Bounding boxes, polygons, keypoints, oriented boxes, classification
• SAM Smart Annotation: Click to generate precise masks using Segment Anything Model
• Keyboard Shortcuts: Efficient workflows with hotkeys

See Annotation for the complete guide.

What export formats are supported?

The Platform supports 17 deployment formats:

See Models Export, the Export mode guide, and the Integrations index for format-specific options.

Troubleshooting

Dataset Issues

Training Issues

Deployment Issues

Common Questions

📅 Created 1 month ago ✏️ Updated 5 days ago

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