NatML for JavaScript

Zero Configuration Machine Learning Deployment

Effortless Machine Learning

NatML is a platform that brings extremely easy and quick machine learning to JavaScript. It allows you to run ML-powered features in your JS application--browser or NodeJS--without managing any ML infrastructure. In a few steps:

First, fetch a model:

// Fetch the ResNet image classifier model data
const modelData = await MLModelData.fromHub("@natsuite/resnet18");
// Create the model
const model = modelData.deserialize();

Then create a predictor to make predictions with the model:

// Create the ResNet classifier predictor
const predictor = new ResNet18Predictor(model, modelData.labels);

Finally, make predictions with the predictor:

// Create an image feature
const imageFeature = new MLImageFeature("/path/to/cat 🐱.jpg");
// Classify the cat image with our predictor
const [label, score] = await predictor.predict(imageFeature);
// Log the results to the console
console.log(`Our image contains a ${label} with confidence ${score}`);

Zero Configuration Deployment

NatML is designed for developers to quickly integrate ML functionality into their applications without any knowledge or effort put into building or managing ML infrastructure. Features include:

Extremely Easy to Use. NatML exposes machine learning models with simple functions that return familiar data types.
Bare Metal Performance. NatML runs predictions on powerful GPU's with incredibly high memory and network bandwidth, giving you the highest performance and minimal latency.
Computer Vision. NatML supports models for object classification, object detection, semantic segmentation, style transfer, and so much more.
Lightweight Package. NatML is distributed in a self-contained package weighing less than 100kb, with minimal external dependencies and no setup necessary.

Get NatML

# Install NatML
npm i natml --save

NextGetting Started

Last updated 3 years ago

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Effortless Machine Learning

First, fetch a model:

// Fetch the ResNet image classifier model data
const modelData = await MLModelData.fromHub("@natsuite/resnet18");
// Create the model
const model = modelData.deserialize();

Then create a predictor to make predictions with the model:

// Create the ResNet classifier predictor
const predictor = new ResNet18Predictor(model, modelData.labels);

Finally, make predictions with the predictor:

// Create an image feature
const imageFeature = new MLImageFeature("/path/to/cat 🐱.jpg");
// Classify the cat image with our predictor
const [label, score] = await predictor.predict(imageFeature);
// Log the results to the console
console.log(`Our image contains a ${label} with confidence ${score}`);

Zero Configuration Deployment

NatML is designed for developers to quickly integrate ML functionality into their applications without any knowledge or effort put into building or managing ML infrastructure. Features include:

Extremely Easy to Use. NatML exposes machine learning models with simple functions that return familiar data types.

Bare Metal Performance. NatML runs predictions on powerful GPU's with incredibly high memory and network bandwidth, giving you the highest performance and minimal latency.

Latest Models. offers a growing catalog of the latest ML models being published from research labs around the world.

Computer Vision. NatML supports models for object classification, object detection, semantic segmentation, style transfer, and so much more.

Lightweight Package. NatML is distributed in a self-contained package weighing less than 100kb, with minimal external dependencies and no setup necessary.