Overview: Watson Machine Learning

Using IBM Watson Machine Learning, you can build analytical models and neural networks, trained with your own data, that you can deploy for use in applications.

Watson Machine Learning provides a full range of tools and services so you can build, train, and deploy Machine Learning models. Choose from tools that fully automate the training process for rapid prototyping to tools that give you complete control to create a model that matches your needs.

New Watson Machine Learning features and plans

Starting on September 1, 2020, Watson Machine Learning will provide updated service plans and service instances to support new features and capabilities.

Changes to service instance credentials and authentication

Attention: The new V2 Watson Machine Learning service instance uses new, simplified authentication. Obtaining bearer tokens from IAM is now performed using a generic user apikey instead of a Watson Machine Learning specific apikey. It is no longer necessary to create specific credentials on the Watson Machine Learning instance. For details, see Authentication.

During the migration period, you can use existing Watson Machine Learning service credentials to access your legacy V1 service instance and assets. Lite users of instances provisioned before Sept 1st can keep using existing credentials during the migration period but cannot generate new credentials. Standard and Professional plan users can follow the steps in Generating legacy Watson Machine Learning credentials to create new credentials.

Your action required

You will be required to migrate your assets from your Watson Machine Learning repository to your Watson Studio project or to a new deployment space and update your Watson Machine Learning service to use the latest features. An automated migration assistant will guide you through moving Watson Machine Learning assets to a project. Alternatively, you can use a dedicated set of Watson Machine Learning APIs to migrate assets programmatically.

After migration, you can access new features, such as:

This graphic illustrates a typical process for a machine learning model.

Building a Machine Learning model

Note that some tools, such as AutoAI, do some data refining as part of the model building process, but generally you need to prepare data prior to using it in a model. For details, see Preparing data.

IBM Watson Machine Learning architecture and services

Watson Machine Learning is a service on IBM Cloud with features for training and deploying machine learning models and neural networks. Built on a scalable, open-source platform based on Kubernetes and Docker components, Watson Machine Learning enables you to build, train, deploy, and manage machine learning and deep learning models using:

Deploying and managing models with Watson Machine Learning

Watson Machine Learning supports popular frameworks, including: TensorFlow, Caffe, PyTorch, and Keras to build and deploy models.

To build and train a model:

Using the tools available for deploying and managing models, you can:

Deployment infrastructure

Programming Interfaces

  • Use Python client library to work with all of your Watson Machine Learning assets in a notebook.
  • Use REST API to call methods from the base URLs for the Watson Machine Learning API endpoints. When you call the API, use the URL and add the path for each method to form the complete API endpoint for your requests.
    • Dallas: https://us-south.ml.cloud.ibm.com
    • London - https://eu-gb.ml.cloud.ibm.com
    • Frankfurt - https://eu-de.ml.cloud.ibm.com
    • Tokyo - https://jp-tok.ml.cloud.ibm.com