How I built Machine learning with Amazon Personalize and a Customer Data Platform

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By making off-the-rack machine learning models accessible for anyone to use, cloud ML services like Amazon Personalize help make ML-driven customer experiences available to teams at any scale. By @mparticle. You no longer need in-house data science and machine learning experts to get the benefit of propensity scoring or product recommendations.

The article then describes:

  • Key challenges with machine learning
  • Collecting and supplying quality user data
  • Making ML insights available and actionable
  • The project: personalized product recommendations
  • Collect data with a CDP
  • Capture product interactions
  • Create the AWS assets
  • Create a Kinesis stream

… and more. Once we’ve set up the infrastructure to generate, continuously refine, and activate ML insights, the final piece of the puzzle is to figure out what works and what doesn’t. You will also get code and screen shots explaining the steps in the article. Well done!

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Tags aws machine-learning learning big-data data-science