How Instacart Modernized the Prediction of Real Time Availability for Hundreds of Millions of Items While Saving Costs

How It's Made

How Instacart Modernized the Prediction of Real Time Availability for Hundreds of Millions of Items While Saving Costs

This is Part 2 of a three-part blog post series in which we outline how we addressed inventory challenges through product, machine learning, and engineering innovations. See Part 1 here. Introduction At Instacart, we serve customers with…...

Jul 17, 2023
Read time: 12min
How Instacart’s Item Availability Evolved Over the Pandemic

How It's Made

How Instacart’s Item Availability Evolved Over the Pandemic

This is the part-1 of a three-part blog post series in which we outline how we addressed inventory challenges through product, machine learning, and engineering innovations. A few years ago, we introduced the problem of predicting…...

Jul 10, 2023
Read time: 9min
How Instacart Measures the True Value of Advertising: The Methodology of Ad Incrementality

How It's Made

How Instacart Measures the True Value of Advertising: The Methodology of Ad Incrementality

Author: Jason Kim In today’s highly competitive digital marketplace, understanding the true value of the advertising effort is no longer a luxury but a necessity. While there are many methodologies used to evaluate the effectiveness…...

Jun 30, 2023
Read time: 7min
Using Contextual Bandit models in large action spaces at Instacart

How It's Made

Using Contextual Bandit models in large action spaces at Instacart

Authors: David Vengerov, Vinesh Gudla, Tejaswi Tenneti, Haixun Wang, Kourosh Hakhamaneshi At Instacart, we strive to provide our customers with the most personalized experience possible by combining multiple considerations they may have when searching for products on Instacart. These…...

Jun 15, 2023
Read time: 15min
Building a Flink Self-Serve Platform on Kubernetes at Scale

How It's Made

Building a Flink Self-Serve Platform on Kubernetes at Scale

Author: Sylvia Lin At Instacart, we have a number of data pipelines with low-latency needs that handle over two trillion events a year. Those events help our engineering and product teams to make better decisions…...

Apr 28, 2023
Read time: 8min
Distributed Machine Learning at Instacart

How It's Made

Distributed Machine Learning at Instacart

How Instacart uses distributed Machine Learning to efficiently train thousands of models in production Author: Han Li At Instacart, we take pride in offering a diverse range of machine learning (ML) products that empower every…...

Mar 24, 2023
Read time: 10min
Adopting PgCat: A Nextgen Postgres Proxy

How It's Made

Adopting PgCat: A Nextgen Postgres Proxy

Authors: Mostafa Abdelraouf, Zain Kabani, Andrew Tanner In this post, we’ll be talking about PgCat, an open-source Postgresql Proxy that we have been using in production and contributing to. It provides connection pooling, load-balancing, and…...

Mar 13, 2023
Read time: 9min
Getting to Know the Data Scientists at Instacart

How It's Made

Getting to Know the Data Scientists at Instacart

Our Data Science team at Instacart is responsible for all data analytics, insights and experimentation at the company. The team partners with our Product and Engineering teams on all stages of the product life cycle,…...

Dec 21, 2022
Read time: 15min
Personalizing Recommendations for a Learning User

How It's Made

Personalizing Recommendations for a Learning User

A talk by Prof. Hongning Wang as part of Instacart’s Distinguished Speaker Series Co-authored by Haixun Wang and Jagannath Putrevu Recommendation systems are at the heart of Instacart: we want to surface the most appropriate…...

Dec 5, 2022
Read time: 5min
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