Machine learning and AI in Watson Studio
IBM Watson Studio is a collaborative environment with AI tools that you and your team can use to collect and prepare training data, and to design, train, and deploy machine learning models.
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 cannot generate new credentials for a V1 service instance. Standard and Professional plan users can follow the steps in Generating legacy Watson Machine Learning 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.
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.
Using the tools available for deploying and managing models, you can: