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apply(recsys) Conference 2022 | Recommend API: Slack’s Unified End-to-End ML Infrastructure

Posted Dec 12, 2022 | Views 212
# apply(recsys) 2022
# Production Use Case
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SPEAKER
Katrina Ni
Katrina Ni
Katrina Ni
Machine Learning Engineer @ Slack

Katrina Ni is a Machine Learning Engineer in Slack ML Services Team where they build ML platform and integrate ML, e.g. Recommend API, Spam Detection, across product functionalities. Prior to Slack, she is a Software Engineer in Tableau Explain Data Team where they build tools that utilize statistical models and propose possible explanations to help users inspect, uncover, and dig deeper into the viz.

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Katrina Ni is a Machine Learning Engineer in Slack ML Services Team where they build ML platform and integrate ML, e.g. Recommend API, Spam Detection, across product functionalities. Prior to Slack, she is a Software Engineer in Tableau Explain Data Team where they build tools that utilize statistical models and propose possible explanations to help users inspect, uncover, and dig deeper into the viz.

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SUMMARY

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.

In the talk, we’ll dive into the Recommend API, a unified framework the team built over the years that allows us to quickly bootstrap new recommendation use cases. 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.

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Posted Dec 12, 2022 | Views 266
# apply(recsys) 2022
# Feature Stores
# Tecton
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Posted Dec 12, 2022 | Views 310
# apply(recsys) 2022
# Panel Discussion
# Production Use Case