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Build a news recommender application with Amazon Personalize

AWS Machine Learning Blog

Prerequisites To implement this solution, you need the following: Historical and real-time user click data for the interactions dataset Historical and real-time news article metadata for the items dataset Ingest and prepare the data To train a model in Amazon Personalize, you need to provide training data.

ETL 80
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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

To solve this problem, we build an extract, transform, and load (ETL) pipeline that can be run automatically and repeatedly for training and inference dataset creation. The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account. But there is still an engineering challenge.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

You can use these connections for both source and target data, and even reuse the same connection across multiple crawlers or extract, transform, and load (ETL) jobs. To store information in Secrets Manager, complete the following steps: On the Secrets Manager console, choose Store a new secret.