Open Source Archives | Tecton

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Lakehouse: A New Class of Platforms for Data and AI Workloads

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, Apache Hudi, and feature stores like Feast and Tecton. … 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

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 learning at Wikimedia, we started implementing model cards for every model hosted by the Foundation. This talk is a description of our process, motivation, and lessons learned along the way. … Read More

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. … Read More

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 companies and talent are using Hugging Face’s tools, and why the open source approach is particularly powerful in doing so. … 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

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 the desired quality. We introduce RecList, a behavioral-based testing methodology and open source package for RecSys, designed to scale up testing through sensible defaults, extensible abstractions and wrappers for popular datasets. … Read More

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 graphs, and TorchArrow, and lightweight front-end for dispatchable data processing, will be introduced with examples. … 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

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