Learning Path: An introduction to the Model Asset Exchange

Overview

The Model Asset Exchange 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 deployable models that you can run as a microservice locally or in the cloud on Docker or Kubernetes, and trainable models where you can use your own data to train the models.

Objectives

Upon completion of this learning path, you will be able to:

• Find and explore deployable and trainable deep learning models on the exchange
• Deploy a model-serving microservice on Docker
• Deploy a model-serving microservice on the Red Hat OpenShift container platform
• Consume the microservice from a Node.js or Python web application
• Consume the microservice from a Node-RED flow
• Consume the microservice from a serverless application
• Consume the microservice in a web browser using JavaScript
• Complete the sample code patterns for the Model Asset Exchange

Prerequisite knowledge

Since this learning path is designed for the beginner, no prerequisite knowledge is required to begin.

Skill level

The skill level of this learning path is for a beginner.

Estimated time to complete

It will take you approximately 3 hours to complete this entire learning path.

Modules

The following items make up this learning path:

• Article

  Get an introduction to the Model Asset Exchange on IBM Developer

  Learn how MAX 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.

• Tutorial

  Get started with the Model Asset Exchange

  Deploy deep learning models as a microservice and consume them in your applications or services.

• Tutorial

  Deploy deep learning models on Red Hat OpenShift

  Deploy model-serving microservices from the Model Asset Exchange on Red Hat OpenShift.

• Tutorial

  Leverage deep learning in your Node-RED flows

  Process image, video, audio, or text data using deep learning models from the Model Asset Exchange in Node-RED flows.

• Tutorial

  Leverage deep learning in IBM Cloud Functions

  Learn how to monitor a Cloud Object Storage bucket for changes and analyze the data using deep learning microservices

• Blog Post

  Running MAX deep learning models on Raspberry Pi

  Explore some problems with deep learning applications, then see how deep learning on a Raspberry Pi can solve them.