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By Sanjeev Ghimire, Adam Massachi | Published October 22, 2018 - Updated October 22, 2018
Artificial IntelligenceMachine LearningCloud
Learn how to use IBM Watson Machine Learning, Apache Spark, and Watson Studio to quickly build and prototype models, to monitor deployments, and to learn over time as more data becomes available. In this code pattern, you’ll learn how to use these services to create and deploy self-learning Watson Machine Learning models.
Because model deployment is not a one-time event, you can use IBM Watson Studio to retrain a model with new data. Performance monitoring and continuous learning enable machine learning models to retrain on new data that you or other data sources supply. Then, all of your applications and analysis tools that depend on the model are automatically updated because Watson Studio handles selecting and deploying the best model.
This code pattern uses IBM Watson Machine Learning and Watson Studio to help you put machine learning and deep learning models into your application. After loading source data into IBM Db2 Warehouse on Cloud, the Watson Machine Learning service creates a machine learning model and saves the data back to Warehouse. Feedback data is uploaded to the Watson Machine Learning service to continuously learn and evaluate new data. Then, the model data is exposed through an API.
When you have completed this code pattern, you’ll understand how to:
Get the detailed instructions in the README file. These steps show you how to:
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