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.

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

Building a Machine Learning model

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.

Deploying and managing models with Watson Machine Learning

Watson Machine Learning supports popular frameworks, including: TensorFlow, Scikit-Learn, and PyTorch to build and deploy models.

To build and train a model:

Using the tools available for deployment, ModelOps, optimization solutions, and Explainable AI, you can:

Deployment infrastructure

Programming Interfaces