Learning Path: An introduction to the Model Asset Exchange
Learn how to use state-of-the-art deep learning models in your applications or services
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.
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
- Complete the sample code patterns for the Model Asset Exchange
Since this learning path is designed for the beginner, no prerequisite knowledge is required to begin.
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.
The following items make up this learning path:
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.
Deploy deep learning models as a microservice and consume them in your applications or services.
Deploy model-serving microservices from the Model Asset Exchange on Red Hat OpenShift.
Process image, video, audio, or text data using deep learning models from the Model Asset Exchange in Node-RED flows.
Learn how to monitor a Cloud Object Storage bucket for changes and analyze the data using deep learning microservices
- Blog Post
Explore some problems with deep learning applications, then see how deep learning on a Raspberry Pi can solve them.