Get an introduction to the Model Asset Exchange on IBM Developer

The Model Asset eXchange (MAX) on IBM Developer is a place for developers to find and use free, open source, state-of-the-art deep learning models for common application domains, such as text, image, audio, and video processing. The curated list includes a broad selection of deployable (ready-to-use) models and trainable (customize-before-use) models.

Model Asset Exchange landing page on IBM Developer

Deployable deep learning models

Deployable deep learning models on MAX have been researched, evaluated, pre-trained, packaged, and published as Docker container images on Docker Hub and are ready for deployment in local or cloud environments using Kubernetes.

Deployable model architecture

Each model-serving Docker image implements a microservice that exposes a REST-API that applications (or other services) invoke to consume the encapsulated deep learning model.

In the example depicted below, a web application calls the Object Detector microservice, providing an image as input. The microservice processes the image, invokes the model, post-processes the output, and returns the result (“objects that were identified in the image”) in an application-friendly JSON format to the caller.

Consume a model-serving

The caller does not need to know anything about the deep learning model that powers the service, the framework that was used to implement and run the model, or the native model inputs or outputs because these details are hidden by the microservice.

Exploring deployable models

On the model exchange, you can filter deployable models by domain (classifiy audio content, classify video content, and identify entities in images), learn more about the models (underlying research, training data sets, and licensing information), test-drive the model (without having to install anything), and explore deployment and customization options.

A popular model is the Object Detector.

Deployable model home page on MAX

Many models include examples that illustrate how to consume it in an Internet of Things (IOT) application, a serverless application, or a web application, such as this code pattern.

Code pattern for the Object Detector model

Trainable deep learning models

You can train some models using your own data with the help of the Watson Machine Learning service on the IBM Cloud.

To learn more about the Model Asset Exchange, take a look at the “Get started with the Model Asset Exchange” tutorial. It walks you through the model microservice deployment in a local environment and outlines how to consume the service from a web application.

Patrick Titzler