Home
Create a predictive model with Watson Machine Learning
Use IBM's Data Science Experience to build a predictive model with Watson Machine Learning.
Cognitive
You're a busy developer or data scientist and want the fastest path delivering insights to users, but this requires deep expertise in many technology domains. This end-to-end 'data to insights' example walks you through the numerous technologies used to acquire, clean, and explore data, build a predictive machine learning model, make predictions, host the model for consumption, and call the hosted model from a Node.js application. You'll learn about IBM's Watson Machine Learning Service for hosting your trained model on IBM's Cloud, and IBM's Data Science experience, a Cloud based IDE for data science teams; tools that bring together many open-source technologies built for data science and machine learning. Note that this application is used for demonstrative and illustrative purposes only and does not constitute an offering that has gone through regulatory review.
- N/A
(it's in box, and around 50 minutes...)
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. Although this is for demonstrative purposes only, the user can see how to use Watson Machine Learning on a data set comprised of health care metrics to create a predictive model for risk of heart failure. After creating this model, inputs that are entered can be scored to form a prediction for an individual case.
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.
- Artificial Intelligence Code Patterns: Enjoyed this Code Pattern? Check out our other AI Code Patterns.
- Data Analytics Code Patterns: Enjoyed this Code Pattern? Check out our other Data Analytics Code Patterns
- AI and Data Code Pattern Playlist: Bookmark our playlist with all of our Code Pattern videos
- With Watson: Want to take your Watson app to the next level? Looking to utilize Watson Brand assets? Join the With Watson program to leverage exclusive brand, marketing, and tech resources to amplify and accelerate your Watson embedded commercial solution.
- Data Science Experience: Master the art of data science with IBM's Data Science Experience
- Spark on IBM Cloud: Need a Spark cluster? Create up to 30 Spark executors on IBM Cloud with our Spark service