MLOps Desktop

Machine learning. Entirely on your Mac.

Name: MLOps Desktop
Author: MLOps Desktop

Go from a raw CSV to a trained, explained, and served model — no cloud, no setup, and your data never leaves your machine.

Download for Mac Read the docs

Requires macOS 12+, Python 3.9+

MLOps Desktop pipeline showing DataLoader, Data Split, Trainer, and Evaluator nodes with classification metrics

Local only No cloud uploads

Explainable SHAP and metrics included

Ship-ready Local HTTP API

Capabilities

From CSV to Served Model

Everything from loading data to serving predictions, on your laptop.

Build the Pipeline Visually

Connect data loading, splits, feature work, training, evaluation, and export on one canvas.

Tune Without Leaving the App

Run Optuna searches and compare the winning parameters against previous trials.

Keep Every Run Traceable

Tag experiments, track metrics, and return to the model that actually performed best.

Serve Models Locally

Start a local HTTP endpoint and test predictions in the built-in playground.

MLOps Desktop's metrics dashboard showing classification metrics, a confusion matrix, and model explainability

A closer look

Explain Model Behavior

Inspect SHAP plots, feature importance, and evaluation metrics before you trust a model.

See how explainability works

Workflow

How It Works

Three steps from data to deployment.

Load Data

Import CSVs and inspect schema

Configure Model

Choose splits, features, algorithms, and tuning ranges

Train & Deploy

Compare metrics, explain behavior, and serve locally

Built to keep your work yours.

100% private — runs on your Mac

Free & open source

Works fully offline

No account, no sign-up

FAQ

Everything you might be wondering

Is MLOps Desktop really free?

Yes — it's free and open source under the MIT license. There are no paid tiers, no trial clock, and no account to create.

Where does my data go?

Nowhere. Training, evaluation, and serving all run locally on your Mac. Your datasets and models never leave your machine, and there's no telemetry.

Do I need to know how to code?

No. You build pipelines visually by connecting nodes on a canvas. And when you want to go deeper, the underlying Python and scikit-learn are right there.

What can I actually build?

Classification and regression models: load a CSV, split and engineer features, train, tune with Optuna, explain with SHAP, compare runs, and serve predictions from a local API.

What do I need to run it?

A Mac on macOS 12 or later — Apple Silicon or Intel — and Python 3.9+. Download, open, and you're building within minutes.