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Create a predictive model with Watson Machine Learning
In this Code Pattern, we will use a Jupyter Notebook on IBM Data Science Experience to build a predictive model that demonstrates a potential health care use case.
Cognitive
Two sentence introduction
By Author A and Author B
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When the reader has completed this Code Pattern, they will understand how to:
- Build a predictive model within a Jupyter Notebook
- Deploy the model to IBM Watson Machine Learning service
- Access the Machine Learning model via either APIs or a Nodejs app
- The developer creates an IBM Data Science Experience Workspace.
- IBM Data Science Experience depends on an Apache Spark service.
- IBM Data Science Experience uses Cloud Object storage to manage your data.
- This lab is built around a Jupyter Notebook, this is where the developer will import data, train, and evaluate their model.
- Import data on heart failure.
- Trained models are deployed into production using IBM's Watson Machine Learning Service.
- A Node.js web app is deployed on IBM Cloud calling the predictive model hosted in the Watson Machine Learning Service.
- A user visits the web app, enters their information, and the predictive model returns a response.
- IBM Data Science Experience: Analyze data using RStudio, Jupyter, and Python in a configured, collaborative environment that includes IBM value-adds, such as managed Spark.
- Jupyter Notebook: An open source web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text.
- PixieDust: Provides a Python helper library for IPython Notebook.
- Artificial Intelligence: Artificial intelligence can be applied to disparate solution spaces to deliver disruptive technologies.
- Data Science: Systems and scientific methods to analyze structured and unstructured data in order to extract knowledge and insights.
- Node.js: An open-source JavaScript run-time environment for executing server-side JavaScript code.
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