This represents the majority of our team’s work, whereas model training is a relatively small piece in the puzzle. We aim to deploy and maintain ML models in production reliably and efficiently, which is termed MLOps. This has allowed us to deliver a number of different recommendation models across the product, driving improved customer experience in a variety of contexts. Behind the scenes, these recommenders reuse a common set of infrastructure for every part of the recommendation engine, such as data processing, model training, candidate generation, and monitoring. Instead, we developed a unified framework we call the Recommend API, which allows us to quickly bootstrap new recommendation use cases behind an API which is easily accessible to engineers at Slack. Each one seems like a terrific use case for machine learning, but it isn’t realistic for us to create a bespoke solution for each. Slack, as a product, presents many opportunities for recommendation, where we can make suggestions to simplify the user experience and make it more delightful.
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