The Feature Store: the missing API between Data Engineering Science?
Machine Learning (ML) pipelines are the key building block for productionizing ML code. However, pipelines are often developed as “silos” - features tend not to be easily re-used across pipelines or even within the same pipeline. Silos lead to duplication, unnecessarily re-implementing features and in the worst case correctness problems, if, for example, the features used for training and serving have inconsistent implementations. The Feature Store solves the problem of siloed and ad-hoc machine learning pipelines, by providing a data layer where feature engineering can be separated from the usage of features to generate training data. That is, the Feature Store should provide a clean API separating Data Engineering from Data Science.
In this talk, we will introduce the world’s first open-source Feature Store, built on Hopsworks, Apache Spark, and Apache Hive and targeting both TensorFlow/Keras and PyTorch. We will show how ML pipelines can be programmed, end-to-end, in Python, and the role of the Feature Store as a natural interface between Data Engineers and Data Scientists. In an end-to-end pipeline, we will show how the Feature Store works, and how you can write end-to-end ML pipelines in Python only (if you so choose).
Jim Dowling is the CEO of Logical Clocks AB, as well as an Associate Professor at KTH Royal Institute of Technology in Stockholm. He is the lead architect of Hops, the world’s most fastest and most scalable Hadoop distribution and first Hadoop platform with support for GPUs as a resource. He is a regular speaker at AI industry conferences, and blogs at O’Reilly on AI.
Feast: Feature Store for Machine Learning
Features are key to driving impact with AI at all scales, allowing organizations to dramatically accelerate innovation and time to market. In this talk, speaker Willem Pienaar explains how GO-JEK, Indonesia’s first billion-dollar startup, unlocked insights in AI by building a feature store called Feast, and some of the lessons they learned along the way.
Features are at the heart of what makes these machine learning systems effective. However, many challenges still exist in the feature lifecycle. Developing features from big data is often an engineering heavy task, with challenges in both the scaling of data processes and the serving of features in production systems. Teams also face challenges in enabling discovery, reducing duplication, improving understanding, and providing standardization of features throughout organizations.
Willem explains the need for features at organizations like GO-JEK and discusses the challenges faced in creating, managing, and serving them in production. He describes how in partnership with Google, GO-JEK designed and built a feature store called Feast to address these challenges and explore their motivations, the lessons they learned along the way, and the impact the feature store had on GO-JEK. He also talks about the open source plans for Feast and the roadmap going forward.