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by Shruthi Subbaiah Machimada, Dolph Mathews, Roger Sanders | Published October 19, 2018
AnalyticsArtificial intelligenceData managementData scienceMachine learning
Many companies and individuals struggle to use their on-premises data — the kind of data that lives on a local machine, within your data center, behind your firewall — for machine learning in the cloud. It can be challenging to find a quick, easy, and secure solution for connecting resources in a protected environment to resources in the cloud.
With Watson Studio and Machine Learning, Db2, and Secure Gateway, it is possible to establish a secure, persistent connection between your on-premises data and the cloud to train machine learning models leveraging cloud computing resources like Spark, elastic environments, and GPUs.
In this guide we’ll create an on-premises Db2 database on our local computer, populate it, and connect it to Watson Studio via Secure Gateway. Next, we’ll read buildings violations data from this database and build a model to predict the likelihood that a particular building will fail an inspection based on historical data from the City of Chicago. After we build the model, we’ll deploy it as an API endpoint with Watson Machine Learning that only authorized users can access.
After completing this code pattern, you’ll learn how to:
Get the detailed instructions in the README file. These steps will show you how to:
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