As more and more machine learning models are deployed into production, it is imperative we have better observability tools to monitor, troubleshoot, and explain their decisions. In this talk, Aparna Dhinakaran, Co-Founder, CPO of Arize AI (Ex-Uber …
Building Malleable ML Systems through Measurement, Monitoring & Maintenance
Machine learning systems are now easier to build than ever, but they still don’t perform as well as we would hope on real applications. I’ll explore a simple idea in this talk: if ML systems were more malleable and could be maintained like …
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 …
Panel: Building High-Performance ML Teams
As Machine Learning moves to production, ML teams have to evolve into high-performing engineering teams. Data science is still a central role, but no longer sufficient. We now need new functions (e.g. MLOps Engineers) and new processes to bridge the …
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 …
