MLOps Coffee Sessions #157 with Katrina Ni & Aaron Maurer, MLOps Build or Buy, Startup vs. Enterprise? co-hosted by Jake Noble of tecton.ai.
This episode is sponsored by Tecton - Check out their feature store to get your real-time ML journey started.
// Abstract
There are a bunch of challenges with building useful machine learning at a B2B software company like Slack, but we've built some cool use cases over the years, particularly around recommendations. One of the key challenges is how to train powerful models while being prudent stewards of our clients' essential business data, and how to do so while respecting the increasingly complex landscape of international data regulation.
// Bio
Katrina Ni
Katrina is a Machine Learning Engineer in Slack ML Services Team, where they build ML platforms and integrate ML, e.g., Recommend API, Spam Detection, across product functionalities. Prior to Slack, she was a Software Engineer in Tableau's Explain Data Team, where they built tools that utilize statistical models and propose possible explanations to help users inspect, uncover, and dig deeper into the viz. Aaron MaurerAaron is a senior engineering manager in the infra organization at Slack, managing both the machine learning team and the real-time services team. In six years at Slack, most of which Aaron spent as an engineer, He worked on the search ranking, recommendation, spam detection, performance anomaly detection, and many other ML applications. Aaron is also an advisor to Eppo, an experimentation platform. Prior to Slack, Aaroon worked as a data scientist at Airbnb, earned a Master's in statistics at the University of Chicago, and helped develop econometric models projecting the Obamacare rollout at Acumen LLC.
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// Related Links
--------------- ✌️Connect With Us ✌️ -------------
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Connect with Katrina on LinkedIn: https://www.linkedin.com/in/katrina-ni-660b2590/
Connect with Aaron on LinkedIn: https://www.linkedin.com/in/aaron-maurer-4003b638/
Timestamps:
[00:00] Aaron and Katrina's preferred coffee
[00:41] Recommender and System and Jake
[02:06] Takeaways
[05:38] Introduction to Aaron Maurer & Katrina Ni
[06:53] Aaron Maurer & Katrina Ni's Recommend API blog post
[08:36] 10-pole machine learning use case and Rex's use case
[10:14] Genesis of Slack's recommender system framework
[11:47] The Special Sauce
[12:58] Speaking the same language
[15:23] Use case sources
[17:08] Slack's feature engineering
[17:52] Main CTR models
[18:40] Data privacy
[21:33] Slack's recommendations problem
[22:09] Fine-tuning the generative models
[23:30] Cold start problem
[26:02] Underrated
[28:24] Baseline
[28:55] Cold sore space
[30:15] LLMs in Production Conference Part 2 announcement!
[32:32] Data scientists transition to ML
[33:35] Unicorns do exist!
[34:43] Diversity of skill set
[36:02] The future of ML
[38:34] Model Serving
[40:11] MLOps Maturity level
[43:06] AWS Analogy
[45:05] Primary difficulty
[48:07] Wrap up
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