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 …
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 …
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 …
Streamlining NLP Model Creation and Inference
At Primer we deliver applications with cutting-edge NLP models to surface actionable information from vast stores of unstructured text. The size of these models and our applications’ latency requirements create an operational challenge of deploying …
Accelerating Model Deployment Velocity
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 …
Declarative Machine Learning Systems: Ludwig & Predibase
Declarative Machine Learning Systems are a new trend that marries the flexibility of DIY machine learning infrastructure and the simplicity of AutoML solutions. In this talk we will discuss about Ludwig, the open source declarative deep learning …
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 …
How to Draw an Owl and Build Effective ML Stacks
They’re handing us an engine, transmission, breaks, and chassis and asking us to build a fast, safe, and reliable car,” a data scientist at a recently IPO’ed tech company opined, while describing the challenges he faces in delivering ML …
Streaming is just an implementation detail
Microservices are stream processing; whether you’re using Redis, Kafka, or gRPC, you continuously handle events and manage consistency. And given that these are some of the most challenging problems in databases, you’re probably not doing a very good …
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 …
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 …
