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.

IBM Watson Machine Learning service

Watson Machine Learning is a service on IBM Cloud with features for training and deploying machine learning models and neural networks:

• Training infrastructure:
  • GPUs for faster training
  • Hyperparameter optimization for training complex neural networks
  • Distributed training to run training jobs across multiple servers
  • Popular frameworks are supported so you can work with your preferred libraries
• Deployment infrastructure:
  • Deploy trained models - as a web service, for batch processing, or with streaming data
• Interfaces:
  • Python client library
  • Command line interface
  • REST API

IBM Watson Studio

Watson Studio is a collaborative environment with graphical tools for designing, training, deploying, and managing models with your Watson Machine Learning services:

• Model builder guides you, step by step, through building a model that uses Spark ML algorithms
• NeuNets synthesizes a neural network and trains it on your training data without you having to design or build anything by hand
• Spark MLlib modeler presents a graphical view of your model while you build it by combining nodes representing algorithm nodes
• SPSS modeler presents a graphical view of your model while you build it by combining nodes representing objects or actions
• Neural network modeler presents a graphical view of your model while you build it by combining neural network nodes)
• 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