In this talk, Matei will present the role of the Lakehouse as an open data platform for operational ML use cases. He’ll discuss the ecosystem of data tooling that is commonly used to support ML use cases on the Lakehouse, including Delta Lake, …
The dbt Semantic Layer
In this talk, Drew will discuss the dbt Semantic Layer and explore some of the ways that Semantic Layers and Feature Stores can be leveraged together to power consistent and precise analytics and machine learning applications.
More ethical machine learning using model cards at Wikimedia
First proposed by Mitchell et al. in 2018, model cards are a form of transparent reporting of machine learning models, their uses, and performance for public audiences. As part of a broader effort to strengthen our ethical approaches to machine …
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 …
Are Transformers Becoming the Most Impactful Technology of the Decade?
In this presentation, Clément will provide insights into the revolution taking place in the open-source community with machine learning. From the CEO who is on a mission to create the “Github of Machine Learning,” learn how the best-in-class …
Wild Wild Tests: Monitoring Recommender Systems in the Wild
As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points. However, real-world behavior is undoubtedly nuanced, and case-specific tests must be employed to ensure …
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 …
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 …
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 …
PyTorch’s Next Generation of Data Tooling
An overview and lookahead of our data efforts within PyTorch, including our new API extension points to support state-of-the-art ML data processing in both research and production. TorchData, an extensible library for constructing data loading …
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 …
Is Production RL at a tipping point?
Reinforcement Learning has historically not been as widely adopted in production as other learning approaches (particularly supervised learning), despite being capable of addressing a broader set of problems. But we are now seeing an exponential …
