On a global marketplace like Etsy where buyers come to buy unique, varied items from sellers from around the globe, the inventory of items is constantly changing. User preferences also change in real time as they discover the latest selection being …
Financially Responsible Feature Engineering
Anyone who has tried doing machine learning at scale knows it can get expensive. The costs associated with training models using on-demand compute and storing features in low latency databases can quickly get out of hand, and we’re often forced to …
Feature Stores at Spotify: Building & Scaling a Centralized Platform
Over 345 million Spotify users rely on Spotify’s great recommendations and personalized features in 170 different markets around the globe (with 85 of these markets launching in the first part of 2021). Some users even claim Spotify knows their …
Building a Feature Store to Reduce the Time to Production of ML Models
Within the world of software development and especially in the area of Machine Learning, times assigned to analysis, development and deployment are key to the success of an organization. Experts’ work in the Feature Engineering process is one of …
Scaling a Machine Learning Social Feed with Feature Pipelines
In 2018 we launched an experiment to add machine learning to the ranking algorithms on the social feed of the Cookpad application. The results of this experiment were plausible for our users, however the architecture we built for this experiment did …
Tackling Fraud with Tecton
Feature stores enable companies to make the difficult leap from research to production machine learning. At their best, feature stores allow you to code up your features once, then use them for training and production, and share them between models. …
Feature Stores at Tide
After a brief introduction to Tide, we’ll talk about the challenges Tide faced to quickly productionize models, how we decided to move forward with a feature store and how this interacts with rules based engines.
Evolution and Unification of Pinterest ML Platform
As Pinterest grew over time, machine learning use cases proliferated organically across multiple teams, leading to a proliferation of technical approaches with bespoke infrastructure. The ML Platform team has been driving Pinterest Engineering to …
Supercharging our Data Scientists’ Productivity at Netflix
Netflix’s unique culture affords its data scientists an extraordinary amount of freedom. They are expected to build, deploy, and operate large machine learning workflows autonomously with only limited experience in systems or data engineering. …
Scaling Online ML Predictions to Meet DoorDash Logistics Engine and Marketplace Growth
As DoorDash business grows, the online ML prediction volume grows exponentially to support the various Machine Learning use cases, such as the ETA predictions, the Dasher assignments, the personalized restaurants and menu items recommendations, and …
