For iPhone & iPad

Quick ML

A complete data-science studio in your pocket.

Import real datasets, explore and clean them properly, train machine-learning models, and make predictions - the whole workflow, entirely on device. Built for data scientists and students of data science who don’t always have a laptop with Python to hand.

Coming soon to the App Store

Data Health Check suggesting cleaning steps, each with its reason

Dashboard of stat tiles and charts built from a dataset

Prediction form with sliders and dropdowns for each model input

1,000,000+

row datasets stream on device

machine-learning algorithms

statistical tests, plain-English verdicts

accounts, uploads or trackers

No laptop? No problem

Open a dataset the moment you get it

On the train, in a lecture, at a client’s site, on the sofa: import a dataset and have a trained, evaluated model before you’re anywhere near a computer. A million-row CSV is fine - data streams into a local store and never loads into memory at once.

Full descriptive statistics for every column the moment it lands
Histograms, box & violin plots, distribution charts - all expandable to full screen and shareable
Non-destructive: every change is a recorded recipe step, individually undoable and replayed when data refreshes

Data screen showing a one-million-row CSV with its columns and export actions — A million-row CSV, comfortable on a phone.

Connectors

Import anything

Real data comes from everywhere. Quick ML meets it there.

CSV & TSV

Open delimited files of any size - a million rows streams straight into a local store.

Excel

Import sheets from .xlsx workbooks.

Parquet

Columnar files from your data pipelines open natively.

URLs & JSON APIs

Pull data from a web address or API endpoint, and refresh it later.

Kaggle

Browse and download Kaggle datasets with your own API key.

PostgreSQL

Query a database directly - credentials stay in your device Keychain.

Image folders & ZIPs

Folders of images grouped by label sub-folder, ready to train a classifier.

Refresh, append, merge

Sources can be re-imported, stacked, or merged on a key as new data arrives.

Explore & clean

EDA that would make your laptop jealous

Profile, visualise, and clean properly - with the reasoning spelled out at every step.

Data Health Check suggesting cleaning steps with reasons — One-tap Data Health Check

Correlation matrix heat map with strongest pairs — Correlation heat maps & pair plots

Data Health Check profiles every column and suggests cleaning steps, each with its reason - swipe away what doesn’t fit, apply the rest in one tap
Fills, filters, dedupe, outlier trims, find & replace, tidy dates, type changes - every change a replayable recipe step, like a pandas script you can swipe
Derived columns: date parts, bins, ratios, lags, rolling windows, and rich text features - word clouds, sentiment, language, readability, people & places, even AI columns generated by Apple’s on-device intelligence
Nine statistical tests - chi-squared, ANOVA, t-test, Mann-Whitney U and more - with plain-English verdicts
One-tap anonymisation: fake names and emails, scrambled phone numbers, postcode areas, dates to month or year - sensitive data handled before it goes anywhere

Train

Real models, honestly evaluated

Seeded or time-ordered splits, cross-validated auto-tuning, and full evaluation - R² and RMSE, confusion matrices, ROC and AUC, permutation feature importance, learning curves. Compare multiple models head to head.

Regression

Linear regression
Decision tree
Random forest (auto-tuned)
Boosted trees
Neural network you design visually

Classification

Logistic regression
Decision tree, random forest & boosted trees
Neural network (up to 6 hidden layers)
k-nearest neighbours & Naive Bayes
Linear SVM & Apple NLP text classifier

Clustering

k-means (elbow & silhouette charts)
DBSCAN (density)
Agglomerative (Ward)
Gaussian mixture

Trained classification model with accuracy, precision, recall and confusion matrix — Accuracy, precision, recall, F1 and confusion matrix for every model.

How to Do This in Python screen exporting the pipeline as a notebook or script — Your exact pipeline as pandas & scikit-learn code.

Studying data science?

It shows its working

Every screen explains what the numbers mean and why each step matters, from quartiles to overfitting. Then How to Do This in Python exports your exact pipeline as a Jupyter notebook - every step as pandas and scikit-learn code with the reasoning in markdown. What you did on the sofa becomes the coursework, and the concepts transfer straight to the tools you’re learning.

Working data scientist? Take the results with you

Export any trained model as a Core ML .mlmodel and drop it into an Xcode project
Batch predictions over whole files, score held-out sets, compare model versions
Build live dashboards from stat tiles and charts, then export as a PDF
Generate a client-ready PDF report of the entire project in one tap
Export the whole project to share with other Quick ML users

Images mode

Machine learning for photos too

Point Quick ML at folders of labelled images and go from raw pictures to a working classifier without leaving your phone.

Understand your classes

Image and class counts, per-class bar charts, and sample browsers for every label.

Average images

See the average image per class - colour, individual channels, or greyscale - and compare two classes side by side.

Train a classifier

Train an image classifier on device, with accuracy stats and a confusion matrix.

Classify photos

Classify single photos or whole batches, with best match and top-class percentages - and export the .mlmodel.

Private by architecture

Your data never leaves your hands

Not a promise buried in a policy - a consequence of how the app is built.

Everything computes on device

Importing, cleaning, training, predicting - all of it happens on your iPhone or iPad. Your data never touches a server.

No accounts, no analytics

Nothing to sign up for, nothing tracking you. An easy answer when the dataset is sensitive.

Syncs through your iCloud

Projects sync privately between your own devices using your own iCloud storage. We receive nothing.

Credentials stay in the Keychain

Kaggle keys and database passwords are stored only in your device’s secure Keychain.

The details, in full: Quick ML Privacy Policy

Made for iPad

More screen, same studio

The full workflow with a projects sidebar and room for dashboards to breathe.

Quick ML on iPad showing the projects sidebar and a data dashboard

If you have data and a phone,
you have a data-science workstation.