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by Lukasz Cmielowski, Scott D'Angelo | Updated January 24, 2019 - Published January 23, 2019
AnalyticsArtificial intelligenceData scienceDeep learningMachine learning
In this developer code pattern, we will log the payload for a model deployed on a custom model serving engine using Watson OpenScale Python SDK. We’ll use Keras to build a deep learning REST API and monitor with Watson OpenScale.
This pattern describes a method to use Watson OpenScale and a custom machine learning model serving engine. With Watson OpenScale, we can monitor model quality and log payloads, regardless of where the model is hosted. In this case, we use the example of a custom model serving application, which demonstrates the agnostic and open nature of Watson OpenScale.
IBM Watson OpenScale is an open environment that enables organizations to automate and operationalize their AI. OpenScale provides a powerful platform for managing AI and ML models on the IBM Cloud, or wherever they may be deployed, offering these benefits:
When you have completed this code pattern, you’ll understand how to:
Find the detailed steps for this pattern in the README. The steps will show you how to:
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