Table of contents 1. Introduction. 2. What is a Feature Store anyway? 3. Offline Batch Layer 4. Online Serving Layer 5. Closing thoughts Introduction In today's article I will discuss engineering challenges faced by Constructor.io [1] when implementing a feature store. Let's dive in! What is a Feature Store...
Table of contents 1. Introduction. 2. Past actions affect personalized recommendations with delay. 3. Leveraging actions in near real-time to adapt recommendations. 4. Results. Introduction In today's article, I will talk about LinkedIn transitioned from an offline update to a near real-time update of features for recommender systems that power...
Table of contents 1. Introduction. 2. Trading off explainability with other metrics. 3. Inherently explainable models. 4. Improving explainability in non-inherently explainable models. Introduction In this article, I will talk about the need of having explainable and interpretable models. Explainability and interpretability are often used interchangeably, but I'd like to...
Table of contents 1. Introduction. 2. First iteration: on-demand batch processing. 3. Second iteration: enabling online requests with pre-computation. 4. Final system architecture. Introduction In today's article, I will describe how Netflix built a ML Media understanding platform. Let's start from the use cases: 1️⃣Dialogue search An editor should...
Table of contents 1. Introduction 2. Batch model 3. Online model Introduction This week I have came across an interesting architecture from Pinterest to fight abuse [1]. Pins (read: a collection of pictures) with similar images are grouped together and uniquely defined by an hash signature. Then, machine learning models...
Table of contents 1. Introduction 2. The legacy platform: one system on top of the other 3. The new platform: scalable and reliable 4. Closing thoughts Introduction Today's topic will deal with improving Reddit's ML platform, from a non-scalable, failure-prone system to a reliable and highly performant one. This system...
