// Abstract

In this Lighting Talk, Peeyush Agarwal explains 2 key pieces of the ML infrastructure at Chime. Peeyush goes into detail about the current feature store design and feature monitoring process along with the ML monitoring setup.

This Lighting Talk is brought to you by arize.com reach out to them for all of your ML monitoring needs.

// Bio

Peeyush Agarwal is the Lead Software Engineer, ML Platform at Chime. He leads the team which enables data science all the way from exploration, model development, and training to orchestrating batch and real-time models in shadow and production. Earlier, Peeyush was a founding engineer in Chime's DSML team and worked on both building models and getting them into production.

Before Chime, Peeyush was a software engineer at Google where he developed unsupervised ML models that run on Google's data across search, Chrome, YouTube, and other properties to identify intent and use it for personalized ads and recommendations. At Google, he also worked on ML-powered Adaptive Brightness and Adaptive Battery which were launched into Android. Prior to joining Google, Peeyush was an entrepreneur who founded a customer engagement platform that counted Aurelia, Reebok, W, and Red Chief among its clients.

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Timestamps:

[00:00] Introduction to Peeyush Agarwal

[01:08] Agenda

[01:27] What Chime is and what Chime do

[01:44] Chime's products

[02:27] Data Science and Machine Learning at Chime

[08:06] Chime's first real-time model

[08:09] Preventing fraud on Pay Friends

[11:01] Feature Store: Unblock real-time capability  

[12:40] Preventing fraud on Pay Friends: Monitoring

[13:35] Preventing fraud on Pay Friends: Instrumentation

[14:36] Monitoring: 4 diverse ways to triage

[15:27] Examples of Metrics: Feature and Model Metrics

[16:39] Scaling Real-time ML at Chime

[17:09] Scaling Real-time ML: Monitoring and Alerting

[18:28] Scaling Real-time ML: Build tools

[20:13] Scaling Real-time ML: Infrastructure Orchestration

[21:36] Scaling Real-time ML: Lessons