How It's Made
How It's Made
Introducing Griffin 2.0: Instacart’s Next-Gen ML Platform
Authors: Rajpal Paryani, Han Li, Sahil Khanna, Walter Tuholski Background Griffin is Instacart’s Machine Learning (ML) platform, designed to enhance and standardize the process of developing and deploying ML applications. It significantly accelerated ML adoption at Instacart by tripling the number of ML applications within a year. In our earlier post, we explained how a combination of MLOps tools allowed us to manage features efficiently, train models, evaluate performance, and…...
Nov 22, 2023
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
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
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
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
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
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
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
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
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