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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