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We provide a weekly round-up of the best Machine learning systems and distributed architecture. Learn how the best engineers scale their ML systems. Delivered every Sunday straight to your inbox!
#4 How Yelp predicts Wait Time for your favourite restaurant?
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 s…
Machine learning at scaleLudovico Bessi
#3 Technical debt in Machine learning systems has very high interest rate.
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…
Machine learning at scaleLudovico Bessi
#2 How TikTok Real Time Recommendation algorithm scales to billions?
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
Machine learning at scaleLudovico Bessi
#1 What is my plan with Machine learning at scale?
Hey! I am Ludo. I am a machine learning engineer at Google. I have created “Machine learning at scale” to talk about ML systems in the real world. I find that the majority of online ML content is divided in these two groups: * Introduction to model X in Python. * An
Machine learning at scaleLudovico Bessi