Sample apps using the models trained in the MNIST tutorials

This topic lists sample apps that demonstrate how to use the IBM Watson Machine Learning Python client or REST API to use deployed models or functions to recognizes hand-drawn digits.

Attention: This sample runs with a deprecated service instance.

This sample is designed to work with a deprecated V1 machine learning service instance. It will fail with a V2 service instance, provisioned after September 1, 2020.

During the migration period, you can still run tutorials and examples associated with a legacy v1 Watson Machine Learning service instance. However, note the following requirements and restrictions:

  • You must use a v1 service instance and associated credentials to run a v1 sample or example. Follow the authentication steps to authenticate with deprecated samples.
  • Lite users can use existing v1 service credentials, but cannot create new credentials.
  • Standard and Professional users can use existing v1 service credentials and can also create new v1 service credentials. The Credentials page was removed from the IBM Cloud Services catalog, so follow the steps in Generating legacy Watson Machine Learning credentials to create new credentials using the IBM Cloud CLI.

Prerequisite

These apps use models created in the MNIST tutorials:

  • The Core ML app uses the Keras model built in the flow editor tutorial
  • The Node.js and Python Flask apps use the TensorFlow model built in any of the tutorials other than the flow editor tutorial and the model builder tutorial

 

Table 1. Sample apps using models created in the MNIST tutorials
Sample app instructions Features demonstrated Files to download
MNIST sample function deployment
  • IBM Watson Machine Learning Python client
  • Online model deployment
  • Online function deployment
MNIST Node.js sample app
  • IBM Watson Machine Learning REST API
  • Online model deployment
  • Online function deployment
MNIST Python Flask sample app
  • IBM Watson Machine Learning Python client
  • Online model deployment
  • Online function deployment
MNIST Core ML sample app
  • Core ML