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.
- For migration dates and announcements of new plans and features, see What’s New.
- For more information on new plans, see Watson Machine Learning plans and compute usage.
- For information on the migration plan, see Migrating assets.
After migration, you can access new features, such as:
- Deployment spaces for organizing the assets for deploying models and functions.
- More deployment options, such as enhanced batch deployment jobs and support for new inputs.
- New REST API and Watson Machine Learning Python client library for working with your Watson Machine Learning assets.
This graphic illustrates a typical process for 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 Studio tools
Watson Studio is a collaborative environment with graphical tools for designing, training, deploying, and managing models with your Watson Machine Learning services:
- AutoAI experiments automatically preprocesses your data, selects the best estimator for the data, and then generates model candidate pipelines for you to review and compare. Deploy the best performing pipeline as a machine learning model.
- SPSS modeler presents a graphical view of your model while you build it by combining nodes representing objects or actions
- Notebooks provide an interactive programming environment for working with data, testing models, and rapid prototyping
- Experiment builder automates running hundreds of training runs while tracking and storing results
- Decision Optimization model builder guides you through building and solving prescriptive models
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, deploy, and manage machine learning and deep learning models using:
- 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
- Distributed training to run training jobs across multiple servers.
- Popular frameworks are supported so you can work with your preferred libraries.
- GPUs for faster training.
- Hyperparameter optimization for training complex neural networks