Watson Machine Learning samples

These samples demonstrate using IBM Watson Machine Learning features and IBM Watson Studio tools.

Model builder tutorials

The model builder tutorials demonstrate the basic steps for using the model builder in Watson Studio:

  1. Upload training data
  2. Choose the machine learning technique and algorithms
  3. Train and evaluate the model
  4. Deploy the model to IBM Cloud
  5. Use the deployed model to make predictions

No coding is required to complete any of the model builder tutorials. You can complete any one of these tutorials in less than 20 minutes.

See: Model builder tutorials

 

MNIST tutorials

The MNIST tutorials demonstrate basic Watson Machine Learning functionality:

  1. Train a model using Watson Machine Learning specialized, high-performance infrastructure
  2. Deploy a model to Watson Machine Learning
  3. Use the deployed model to classify new images

Multiple tools and features are demonstrated in the MNIST tutorials:

  • Different machine learning and deep learning frameworks
  • Model-building graphical tools in Watson Studio
  • Building models in Python code
  • Watson Machine Learning Python client
  • Watson Machine Learning CLI

See: MNIST tutorials

 

MNIST sample apps

The MNIST samples apps demonstrate how to use models trained in the MNIST tutorials in an application:

  • Deployed function
  • Node.js app
  • Python Flask app
  • Core ML app

See: MNIST sample apps