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.

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

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.

You can build and train a model, using one of the tools listed in Analyzing data and building models, or you can import a model you built and trained outside of Watson Studio.

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