All ML teams need to be able to translate offline gains to online performance. Deploying ML models to production is hard. Making sure that those models stay fresh and performant can be even harder. In this talk, we will cover the value of regularly …
Compass: Composable and Scalable Signals Engineering
Abnormal Security identifies and blocks advanced social engineering attacks in an ever-changing threat landscape, and so rapid feature development is of paramount importance for staying ahead of attackers. As we’ve scaled our machine learning system …
Why is Machine Learning Hard?
Each of us has a different answer for “why is machine learning so hard.” And how long you have been working on ML will drastically influence your answer. I’ll share what I learned over the past 20 years, implementing everything from scratch …
Extending Open Source Feature Stores to Fit Adyen
We walk you through how we adopted Feast at Adyen. We’ll discuss the decisions we made because of infra and tech constraints, and the customizations we added— in particular for our open source project, spark-offline-store, which was adopted into …
Using Feast in a Ranking System
This will be a practical session explaining how Better.com uses Feast in a Ranking System that depends on multiple data sources and several models. We’ll provide a walkthrough of several architectures we considered as a team to manage features, …
Model Calibration in the Etsy Ads Marketplace
When displaying relevant first-party ads to buyers in the Etsy marketplace, ads are ranked using a combination of outputs from ML models. The relevance of ads displayed to buyers and costs charged to sellers are highly sensitive to the output …
How Robinhood Built a Feature Store Using Feast
Features are essential to ML models. Therefore, a good feature infrastructure is important to any organization that wants to use ML properly in production. Feast is a great tool for building up your feature infrastructure. However, using Feast in …
How Shopify Contributed to Scale Feast
This talk will discuss how Shopify manages large volumes of ML data (billions of rows) using Feast. Shopify decided to adopt Feast to build their ML Feature Store in early 2021. We will speak about how we contributed to Feast to make it more scalable …
Streaming Architecture with Kafka, Materialize, dbt, and Tecton
Drizly is building out our Data Science stack and streaming infrastructure to match the success we’ve had with the modern data stack on BI. We are currently standing up our architecture using Kafka, Materialize, and dbt. We are planning on adding …
Real-time Personalization of QuickBooks using Clickstream Data
In this session, we will talk about Intuit’s real-time personalization ML pipeline. We will use a self-help use case to show how Intuit provides proactive self-help to millions of users by personalizing content based on user behavior to increase …
Building a Best-in-Class Customer Experience Platform – The Hux Journey – Deloitte Digital
New technologies have been advancing rapidly across the areas of frictionless data ingestion, customer data management, identity resolution, feature stores, MLOps and customer interaction orchestration. Over the same period many large enterprises …
Exploiting the Data Code: Duality Applying Modern Software Development Practices to Data with Dali
Most large software projects in existence today are the result of the collaborative efforts of hundreds or even thousands of developers. These projects consist of millions of lines of code and leverage a plethora of reusable libraries and services …
