For questions relating to recommendation engines, collaborative filtering, and personalization. Questions tend to be algorithmic or statistical in nature.

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When you’re browsing sites like Netflix or Amazon, you get recommendations on what to watch or buy next. This is actually a big deal, and companies offer huge prizes to make these algorithms better.

This reference architecture shows how to train a recommendation model using Azure Databricks and deploy it as an API by using Azure Cosmos DB, Azure Machine Learning, and Azure Kubernetes Service (AKS). This architecture can be generalized for most recommendation engine scenarios, including recommendations for products, movies, and news.