Pretrained computer vision classifier

Identify flight delays with one API call.

A pretrained flight delays classifier that sorts an image into one of 3 categories. Use the flight delays API immediately, no training required, then adapt it to your own data when you need more.

Pretrained · Nyckel-trained 3 labels out of the box Tabular input

What this flight delays classifier recognizes

A sample of the 3 labels this pretrained classifier chooses between.

Not Likely
Likely
Very Likely

Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.

Call the flight delays API

Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:

curl

curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
  -H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"data": "https://example.com/photo.jpg"}'

Python

import requests

# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"

response = requests.post(
    "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
    headers={"Authorization": "Bearer " + token},
    json={"data": "https://example.com/photo.jpg"},
)
print(response.json())

Example response

{
  "labelName": "Not Likely",
  "labelId": "label_...",
  "confidence": 0.92
}

Under the hood

Model type
Nyckel-trained

Trained on a Nyckel-curated dataset covering 3 flight delays categories, served on Nyckel's own infrastructure — your image stays on Nyckel.

Input
Tabular

Send structured fields to the invoke endpoint; the response is a label with a confidence score.

Make it yours
Adaptable

Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.

More than a demo: this page is one of thousands of pretrained functions on Nyckel, an ML classification platform. You can invoke classifiers by API, review predictions, correct labels, collect samples from production traffic, and promote any pretrained function to a private custom model — without changing your integration.

Where teams use flight delays classification

Travel agencies

Predict the likelihood of flight delays to inform customers and adjust booking options accordingly.

Airlines

Provide passengers with real-time updates and alternatives in case of potential flight delays.

Customer service solutions

Automate responses and solutions for customers dealing with delayed flights.

Insurance companies

Offer tailored travel insurance policies based on flight delay risks.

Logistics and shipping

Adjust logistics plans based on expected flight delays to ensure timely deliveries.

Event planning services

Prepare for attendee delays or absences at events due to flight delays.

Hospitality industry

Manage room bookings and staff allocation in response to changes in guest arrivals due to flight issues.

Common questions

What's the difference between a zero-shot and a Nyckel-trained classifier?

A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.

How do I know whether this will work for my application?

Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.

What happens when it makes a mistake?

No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.

Do I need training data to get started?

No. This flight delays classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.

What does it cost to try?

Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.

Ready to classify flight delays at scale?

Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.