index.md

copyright	years 2016, 2017
lastupdated	2017-08-09

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Getting started with Watson Machine Learning

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Use IBM® Watson™ Machine Learning to integrate predictive analytics with your applications. Data scientists use machine learning to develop predictive models, whereas developers use machine learning to create applications that make smarter decisions, solve tough problems, and improve user outcomes. {: shortdesc}

Prerequisites

To use Watson Machine Learning, from the Bluemix catalog, you must create the service instance here.

Steps

1. Create and store a model.
2. Deploy a model.
3. Use the deployed model scoring endpoint in your application to get predictions.

About

The Watson Machine Learning service is a set of REST APIs that can be called from any programming language.

The focus of the Watson Machine Learning service is deployment, but note that IBM SPSS Modeler or Data Science Experience is required for authoring and working with models and pipelines. Both SPSS Modeler and Data Science Experience (leveraging Spark MLlib and Python scikit-learn) offer various modeling methods that are taken from machine learning, artificial intelligence, and statistics.

Related Links

Ready to get started? To create an instance of a service or bind an application, see Using the service with Spark and Python models or Using the service with SPSS models.

If you are interested in exploring the API, see Service API for Spark and Python models or Service API for SPSS models.

For details about SPSS Modeler and the modeling algorithms it provides, see IBM Knowledge Center.

For details about IBM Data Science Experience and the modeling algorithms it provides, see https://datascience.ibm.com.