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This chapter is from the book

Cloud Solutions to Recommendation Systems

The Google Cloud Platform has an example of using ML on Compute Engine to make product recommendations (https://cloud.google.com/solutions/recommendations-using-machine-learning-on-compute-engine) that is worth exploring. In the example, PySpark and the ALS algorithm are used along with proprietary cloud SQL. Amazon also has an example of how to build a recommendation engine using their platform, Spark and Elastic Map Reduce (EMR) (https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/).

In both cases, Spark is used to increase the performance of the algorithm by dividing the computation across a cluster of machines. Finally, AWS is heavily pushing SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html), which can do distributed Spark jobs natively or talk to an EMR cluster.

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