Zanimljiv post o tome kako su inženjeri Slack-a napravili ML-powered API za nadovezivanje različitih preporuka koje se koriste u aplikaciji.

Slack, as a product, presents many opportunities for recommendation, where we can make suggestions to simplify the user experience and make it more delightful. 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.

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. 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. This has allowed us to deliver a number of different recommendation models across the product, driving improved customer experience in a variety of contexts.