Feature Stores Archives | Tecton

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Enabling rapid model deployment in the healthcare setting

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. … Read More

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. David and Miles will provide a demo of the Tecton and Snowflake integration along with coding examples. Attendees will learn how to:

– Build new features using Tecton’s declarative framework

– Automate the transformation of batch data directly on Snowflake

– Automate the transformation of real-time data using Snowpark

– Create training datasets from data stored in Snowflake

– Serve data online using DynamoDB or Redis … Read More

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 streaming feature maintenance that optimizes both resource costs and downstream model accuracy. We introduce a notion of feature store regret to evaluate feature quality of different maintenance policies, and test various policies on real-world time-series data. … Read More

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 happening openly through public forums. However, designing out in the open hasn’t always been straightforward. As the Feast user base has grown, maintainers of the project have been faced with new and interesting challenges. In this talk we will share three examples of when the Feast community surprised us, and how that impacted the project’s direction. … Read More

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 data. We’ll provide coding examples to showcase how Feast can be used to:

– Curate features in online and offline storage

– Process features in real-time

– Ensure data consistency between training and serving environments

– Serve feature data online for real-time inference

– Quickly create training datasets

– Share and re-use features across models … Read More

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 learning and data-related lessons and challenges we’ve encountered over the last 5 years on Faire’s journey to empowering entrepreneurs to chase their dreams. … Read More

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 use cases. We provide plug-and-play feature store and model hosting backends to meet various needs. We will share our experience on optimizing our service. … Read More

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 resources to process data in real-time. Instead, you can write SQLs to do the processing and analysis on streaming data.

At Gojek, Data Platform team use Dagger for feature engineering on realtime features. Computed features are then ingested to Feast for model training and serving. Dagger powers more than 200 realtime features at Gojek. This talk will about the end to end architecture and how Dagger and Feast work together to provide a cohesive feature engineering workflow. … Read More

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 to serve thousands of features for hundreds of machine learning models, it’s become a major focus to balance stability with rapid iteration speed. Last year at apply(), we saw a great talk from Stitch Fix on their Hamilton framework for managing complicated logic in Pandas Dataframes. In this talk, we present a similar framework called Compass that was developed at Abnormal. Compass takes a similar approach to Hamilton by modeling feature extraction pipelines as a DAG of composable functions, but differs in some key design choices that make it a better fit for Abnormal’s ML use-case and tech stack. We’ll show how Compass enables machine learning engineers to express feature extraction logic in simple, pipeline-agnostic Python functions, while also providing a way to interface with a feature store in a scalable way when needed. … Read More

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 the main feast repo. We hope our journey can help you reason about adopting Feast into your stack. … Read More

Redis? Dynamo? Cassandra? How to choose the right online store for your ML features.

Where should you store your ML features to power real-time ML predictions, and why? In this talk, Tecton’s Co-Founder and CTO, Kevin Stumpf, will discuss the tradeoffs made and lessons learned while building the Feature Stores at Uber, Tecton and Feast.

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