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:

Building and deploying models with Watson Machine Learning

Ranging from graphical tools you can use to build a model in minutes, to tools that automate running thousands of experiment training runs and hyperparameter optimization, Watson Studio AI tools support popular frameworks, including: TensorFlow, Caffe, PyTorch, and Keras.

You can think of Watson Studio AI tools in three categories:

  • Visual recognition
  • Natural language classification
  • Watson Machine Learning

Visual recognition

Tools focused specifically on visual recognition:

  • Built-in models you can use to analyze images for scenes, objects, and many other categories without any training..
  • A model builder makes it quick and easy to train a model to classify images according to classes you define.
  • Core ML support for using your visual recognition custom models in iOS apps.

See: Visual recognition

Natural language classification

Tools focused specifically on natural language classification:

  • A model builder makes it quick and easy to train a model to classify text according to classes you define.
  • API for classifying text in notebooks or in apps you develop.

See: Natural language classification

Watson Machine Learning

Tools for designing, training, and managing models:

  • AutoAI automatically analyzes your data and generates candidate model pipelines customized for your predictive modeling problem. .
  • SPSS Modeler uses the SPSS flow editor so that you can quickly develop predictive models using business expertise and deploy them into business operations to improve decision making.
  • Experiment builder automates running hundreds of training runs while tracking and storing results.
  • Notebooks provide an interactive programming environment for working with data, testing models, and rapid prototyping.
  • Machine learning command line interface lets you build and work with models in your local environment.
  • Decision Optimization model builder guides you through building and solving prescriptive models.

See: Watson Machine Learning