Discover how Vital powers its predictive, customer-facing, emergency department wait-time product with request-time input signals and how it solves its “cold-start” problem by building machine-learning feedback loops using Tecton.
DIY Feature Store: A Minimalist’s Guide
A feature store can solve many problems, with various degrees of complexity. In this talk I’ll go over our process to keep it simple, and the solutions we came up with.
Workshop: Operationalizing ML Features on Snowflake with Tecton
Many organizations have standardized on Snowflake as their cloud data platform. Tecton integrates with Snowflake and enables data teams to process ML features and serve them in production quickly and reliably, without building custom data pipelines. …
ralf: Real-time, Accuracy Aware Feature Store Maintenance
Feature stores are becoming ubiquitous in real-time model serving systems, however there has been limited work in understanding how features should be maintained over changing data. In this talk, we present ongoing research at the RISELab on …
Lessons learned from the Feast community
Feast, the open source feature store, has seen a dramatic rise in adoption as ML teams build out their operational ML use cases. The growth that Feast has experienced is in part due to the project being a community-driven effort, with development …
Empowering Small Businesses with the Power of Tech, Data, and Machine Learning
Data and machine learning shape Faire’s marketplace – and as a company that serves small business owners, our primary goal is to increase sales for both brands and retailers using our platform. During this session, we’ll discuss the machine …
Workshop: Building Real-Time ML Features with Feast, Spark, Redis, and Kafka
This workshop will focus on the core concepts underlying Feast, the open source feature store. We’ll explain how Feast integrates with underlying data infrastructure including Spark, Redis, and Kafka, to provide an interface between models and …
Weaver: CashApp’s Real Time ML Ranking System
In this session, we will talk about one of the core infrastructure systems to personalize the experience on the CashApp, Weaver, and the work we did to scale it. Weaver is our real-time ML ranking system to rank items for search and recommendation …
Feature Engineering at Scale with Dagger and Feast
Dagger or Data Aggregator is an easy-to-use, configuration over code, cloud-native framework built on top of Apache Flink for stateful processing of real-time streaming data. With Dagger, you don’t need to write custom applications or manage …
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 …
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 …
Machine Learning Platform for Online Prediction and Continual Learning
This talk breaks down stage-by-stage requirements and challenges for online prediction and fully automated, on-demand continual learning. We’ll also discuss key design decisions a company might face when building or adopting a machine learning …
