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copyright

years
2016, 2017

lastupdated

2017-08-09

{:new_window: target="_blank"} {:shortdesc: .shortdesc} {:screen: .screen} {:codeblock: .codeblock} {:pre: .pre}

Development and persistence of the custom model

Working with custom models

Scenario name: Product line prediction.

Scenario description:

Our client is running one of the most famous chain stores in Europe. They would like us to figure out their clients' interests in terms of their product line such as Personal Accessories, Camping Equipment, and Outdoor Protection. A Data Scientist develops a predictive model and shares it with you (the developer). Your task is to deploy the model and generate predictive analytics by making score requests against the deployed model.

Development and persistence of the custom model

Use Data Science Experience to create custom models. After signing up, sign in and complete the following steps.

Create an organization and a space. The first time you sign in, you'll be asked for it, so click Continue and accept the default values.
After the organization is created, go to My Projects and click create project.
Specify a name and description for your project and click Create. The project name you specified will also be used as your Target Container's name.
After the project is created, you can either:
- add notebooks and start developing your own models, or you can upload one of the following sample notebooks:
- Developing Spark MLlib models with Python
- Developing Spark MLlib models with Scala
- Developing scikit-learn models with Python
- add models and start developing your own models using model wizard

Deployment and scoring of the custom model

See the following sections for instructions regarding deploying and scoring models, or see the scoring section of the notebooks linked to previously.