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: On-Device Machine Learning is hard! 2. First solution: Optimize the size of your Machine Learning model. 3. Second solution: Create two different Machine Learning models. Introduction: On-Device Machine Learning is hard! In the past articles, I have been focusing on large scale systems assuming almost...
Table of contents 1. Introduction. 2. Adversarial examples and robust classifiers. 3. How to generate adversarial examples. 4. How to defend your precious Machine Learning models against adversarial examples. Introduction Today's article will dive deep into adversarial examples. There are two major reasons adversarial examples are important to understand: 1....
Table of contents 1. Introduction. 2. The spectrum of roles. 3. Roles overlap depending on organization's maturity and scope. 4. Closing thoughts. Introduction Today's article will be more on the lighter side. I will discuss the different archetypes of Machine learning roles in industry and how they overlap depending on...
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. Lesson 1: Machine learning as a Swiss knife: the two extremes. 3. Lesson 2: Model performance is not strongly correlated with business performance 4. Lesson 3: Look for a problem, not for a solution 5. Lesson 4: Latency matters 6. Lesson 5: Monitoring for...
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...
Table of contents 1. Introduction 2. Making smart strategies available widely in real-time 3. The Looper platform 4. Adoption and impact Introduction In recent years, there has been a big push by tech companies to scale in-house AI platforms to let software engineers from different teams incorporate machine learning models...
Introduction The main topic of this article is: "How can we fight model drift?" As soon as a model is trained, validated and deployed to production, it begins degrading. Inputs and outputs need to be closely monitored to diagnose and better yet prevent model drift. DoorDash approaches model observability as...
Introduction Lately, there has been a strong focus in tech space on building Machine learning systems that: * Respect privacy. * Avoid harmful bias. * Mitigate unintended consequences. Still, building a transparent and trustworthy system is not easy an easy fit. The focus of this article is going to be on: * Transparency as...
Introduction Today topic is about continuous model deployment. When working with Machine Learning systems, one must always remember that the world around is changing fast. Training a model, deploying it online and calling it a day is not a valid option, as model's performance degrades quickly. If you want to...
Introduction In this article, I will describe how Yelp predicts the waiting time for restaurants around the world. In this setting, latency of the system is paramount: when users want to know the current waiting time, they expect an immediate answer. However, they don't particularly care for the system to...
Introduction The harsh reality of new graduates joining a Machine learning team is that the "model development" part of the job dwarfs in comparison to everything else the team needs to support. The priority of the team is often: improving models' up-time, optimizing the data pipeline, adding new monitoring dimensions...
Introduction In today's article, I am going to dive deep into the paper [1] published by TikTok engineers describing how TikTok real time recommendation system is built. The paper has many topics that are worth learning in more detail, but I am going to focus on: * How the design supports...
Table of contents 1. Introduction. 2. Online predictions with batch features. 3. Online predictions with online features: real time vs near real time. 4. When does it make sense to move things online? Introduction As a follow up of my past article related to Batch Serving: Machine Learning at scale:...
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. Batch prediction in depth. 3. Does it always make sense to try and go online? Introduction In today's article, I will discuss the ideas behind Batch prediction. First of all, what do I mean with it? Batch prediction = offline predictions of ML models computed...
Table of contents 1. Introduction: On-Device Machine Learning is hard! 2. First solution: Optimize the size of your Machine Learning model. 3. Second solution: Create two different Machine Learning models. Introduction: On-Device Machine Learning is hard! In the past articles, I have been focusing on large scale systems assuming almost...
1. Introduction 2. Optimal feature discovery through a centralised Feature Store 3. Results Introduction Today's topic is about feature selection. As a Machine Learning engineer, you are tempted to ingest as many features as possible from different teams in hope to improve your model performance. However, improving model performance by...
Table of contents 1. Introduction. 2. Adversarial examples and robust classifiers. 3. How to generate adversarial examples. 4. How to defend your precious Machine Learning models against adversarial examples. Introduction Today's article will dive deep into adversarial examples. There are two major reasons adversarial examples are important to understand: 1....
