How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time. By: Daochen Zha , Chun How Tan , Xin Liu , Bin Xu , Han Zhao , Xiaowei Liu , Jun Shi , Tracy Yu , Hui Gao , Huiji Gao , Liwei He , Michael Kinoti ,…
#machine learning
57 posts
21 Jul
20 Jul
A look at compensating for position bias in recommender systems using negative sampling strategies By: Andrew Morss, Senior Applied Scientist Introduction A recommender system is a machine learning model that, given a user and a catalog of items, predicts which items that user is most likely to want. Recommenders set your YouTube playlist, determine what items Amazon suggests for you,…
14 Jul
Expedia Group Technology — Innovation A framework for how we build, deploy, and evolve AI systems for impact and scale Photo by Florian Wehde on Unsplash There’s an important distinction between Artificial Intelligence (AI) that just works today and AI that lasts at scale. Many companies optimize hard for the first one without ever asking whether they’re building the second.…
9 Jul
Hands-on guide to coding drift detection in Python: concept drift example, River package, MD3 and HDDDM multivariate detectors with code.
6 Jul
Every year, the International Conference on Machine Learning (ICML) reveals where thousands of AI researchers have decided to put their work. This year’s accepted papers reveal a clear direction: open frontier models and open AI infrastructure have become foundational to how modern AI science gets done. NVIDIA had 74 papers accepted at ICML 2026. Approximately […]
29 Jun
Authors: Lequn Wang , J iangwei Pan , and Linas Baltrunas Figure 1. Autoregressive homepage generation. GenPage builds a Netflix homepage one row or entity at a time, each one conditioned on what’s already on the page and the user’s context. Introduction The Netflix homepage is the first thing users see when they open the app and the primary way…
25 Jun
Sheng Huang | Software Engineer, AI Platform; Pong Eksombatchai | Machine Learning Engineer, Applied Sciences; Saurabh Vishwas Joshi | Software Engineer, AI Platform; Gaurav Arora | Software Engineer, AI Platform; Karthik Anantha Padmanabhan | Engineering Director, AI Platform At Pinterest, foundation models power recommendations for over 600 million monthly active users. Our latest Foundation Model (ACM RecSys 2025) pre-trains on…
19 Jun
Predicting Risk in Content Launches: How Data-Driven Insights can Transform Launch Planning
Netflix Technology Blogby Emily Gill Each year, we bring the Analytics Engineering community together for an Analytics Summit — a multi-day internal conference to share analytical deliverables across Netflix, discuss analytic practice, and build relationships within the community. This post is one of several topics presented at the Summit highlighting the breadth and impact of Analytics work across different areas of the…
by Matthew Wood , Ishan Gupta , Kevin Mercurio, Devon Bryant , and Claire Dorman In his seminal book “Thinking, Fast and Slow,” Daniel Kahneman describes two systems that drive human cognition: System 1, which operates automatically and quickly with little effort, and System 2, which allocates attention to more challenging mental activities requiring deliberate focus. This dual-process theory has…
2 Jun
How Airbnb used sequential geographic recovery signals and prior propagation to generate reliable corridor-level forecasts when local data was scarce. By: Harrison Katz The problem with unprecedented shocks Almost every forecasting system is built on the same implicit assumption: the future will resemble the past. You train on historical data, you validate on holdout periods, and you trust that past…
Author: Paul Coursaux At Criteo, retail media is about helping brands reach shoppers directly on retailers’ property, right at the digital shelf where purchase decisions are made. Through CMAX , our unified retail media platform, we connect advertisers to retailers’ audiences with Sponsored Products that appear alongside native results in onsite search and browsing experiences. Sponsored products: Boost your brand…
25 May
Beyond the Map: Building a Last-Last-Mile Routing System That Learns From Every Delivery
Swiggy BytesAuthor: Aarav Nigam Special thanks to Charan and Meghana Negi for their contribution and guidance throughout this project. Introduction Every delivery has a moment where standard navigation stops being useful. Getting from a restaurant or dark store to the customer’s neighborhood is largely a solved problem. Existing routing systems do that well. The harder part begins after the delivery executive…
21 May
Authors ( listed alphabetically ) Ads Feature Engineering Infra team: Ajay Venkatakrishnan, Le Zhang Core ML Infra team: Eric Shang, Pihui Wei ML Data team: Connor Votroubek, Yi He User Understanding team: Camilo Munoz, Simin Li If you work on ranking, retrieval, or recommendation systems, you’ve probably asked for some version of the same thing: “Give me the last N…
Nova lets engineers run multiple coding sessions in parallel and lets internal systems use AI agents as part of automated workflows.
11 May
One might think computer vision models are supposed to be easy to put into production. There are whole companies built on that promise: label a few images, click train, click deploy, done. In practice, it’s messier. Most of us working with these models aren’t ML experts, and moving fast to keep up with the industry […] The post Lessons from…
4 May
Democratizing Machine Learning at Netflix: Building the Model Lifecycle Graph
Netflix Technology BlogSaish Sali , Nipun Kumar , Sura Elamurugu Introduction As Netflix has grown, machine learning continues to support our ability to deliver value to members and drive excellence across multiple areas of our business. When Netflix began investing in machine learning over a decade ago, it was primarily focused on a single domain: personalization. Scala was the industry standard, our…
1 May
By Nipun Kumar , Rajat Shah , Peter Chng Introduction This is the first blog post in a multi-part series that shares technical insights into how our ML model serving infrastructure powers several personalized experiences at scale across various domains (e.g., title recommendations, commerce). In this introductory blog post, we will dive into our domain-independent API abstraction and its traffic…
Guangtong Bai | Staff Software Engineer, Product ML Infrastructure*; Shantam Shorewala | Software Engineer II, Product ML Infrastructure*; Chi Zhang | Staff Software Engineer, AI Platform*; Neha Upadhyay | Software Engineer II, AI Platform*; Haoyang Li | Director, Product ML Infrastructure *These authors contributed equally to this article. Background At Pinterest, our online ML serving systems employ a root-leaf architecture.…
27 Apr
From Clicks to Conversions: Architecting Shopping Conversion Candidate Generation at Pinterest
PinterestAuthors: Richard Huang | Machine Learning Engineer II; Yu Liu | Senior Machine Learning Engineer; Ziwei Guo | Senior Machine Learning Engineer; Andy Mao | Staff Machine Learning Engineer; Supeng Ge | Sr. Staff Machine Learning Engineer Introduction At Pinterest, conversion ads are crucial for matching users with products they are likely to purchase, boosting value for both users and…
15 Apr
Vaibhav Shankar; Staff Software Engineer | Raymond Lee; Staff Software Engineer | Chia-Wei Chen; Staff Software Engineer | Shunyao Li; Sr. Software Engineer | Yi Li; Staff Software Engineer | Ambud Sharma; Principal Engineer | Saurabh Vishwas Joshi; Principal Engineer | Charles-A. Francisco; Senior Engineer | Karthik Anantha Padmanabhan; Director, Engineering | David Westbrook; Sr. Manager, Engineering One day in…
13 Apr
Authors: Matt Lawhon | Sr. Machine Learning Engineer; Filip Ryzner | Machine Learning Engineer II; Kousik Rajesh | Machine Learning Engineer II; Chen Yang | Sr. Staff Machine Learning Engineer; Saurabh Vishwas Joshi | Principal Engineer At Pinterest, scaling our recommendation models delivers outsized impact on the quality of the content we serve to users. Our Foundation Model (oral spotlight,…
10 Apr
Real-time ML Ranking for Autocomplete: Deploying Learning-to-Rank inside OpenSearch (Part 1) Co-authored with Srinivas Nagamalla . Special mentions to Yawan Gupta and the Search-engineering-team for their contributions. Autocomplete is one of the most latency-sensitive surfaces in any consumer app. At Swiggy, autocomplete is triggered on every keystroke, so ranking has to fit within a tiny latency budget while serving far…
26 Mar
Two tiny AI models. No server. ~300ms. Here’s the story. Authors: Arpit Goel , Shruti Shrivastava The Problem Crew is a conversational concierge — one chat box to book cabs, restaurants, hotels, trips, gifts. No separate screens. Just type what you need. A user types “book cab from airport” and submits. That works well — but a chat box alone…
26 Feb
How we train Dash's search ranking models with a mix of human and LLM-assisted labeling.
12 Feb
Making products like Dropbox Dash accessible to individuals and businesses means tackling new challenges around efficiency and resource use.
6 Jan
Expedia Group Technology — Data Science Empowering developers with seamless vector embedding solutions Photo by Daniela Cuevas on Unsplash Introduction Rapid advances in Machine Learning (ML), especially Generative AI, have increased the need for specialized capabilities like vector embedding similarity search. Vector embeddings are the numerical representations created by machine learning models which allow disparate inputs to be compared against…
18 Dec 2025
The feature store is a critical part of how we rank and retrieve the right context across your work.
26 Nov 2025
Introduction In an age where artificial intelligence (AI) and machine learning (ML) are integral to almost every aspect of our lives, ensuring the effectiveness, fairness, and reliability of ML models is paramount. Observability plays a crucial role in maintaining the performance of these models, allowing us to detect and resolve issues promptly. At Helpshift, we recognized the need for robust…
25 Nov 2025
By Jean V. Alves and Ferran Pla Fernández Moving beyond binary classification provides novel insights. In the real world, scams rarely present themselves in black and white. Fraudsters exploit nuance, impersonate legitimate brands, and mask malicious intent with seemingly ordinary behavior. That’s why Feedzai has launched ScamAlert (patent pending), a Generative AI-based system innovating on the current paradigm of scam…
10 Oct 2025
As a fast-growing home services platform, we heavily utilize machine learning to elevate user experience and improve business processes such as reducing spam, improving search results, and providing recommendations. In recent years, Generative AI has taken the world by storm as a powerful addition to traditional ML. We embraced this mega trend by incorporating LLMs into various areas of our…
26 Aug 2025
How we made our email story recommendations better In this Part 1, you’ll understand how we improved one of the main ways our users are exposed to our product and how that led to a massive 7% increase on the average reading time for the digest users. Intro : This is a 4-part series breaking down improvements to the algorithm…
25 Aug 2025
Cross-Digest diversification In this part 4, we’ll see how we went from investigating a few complaints from digest power users to improving our digest recommendations across the board. Intro : This is a 4-part series breaking down improvements to the algorithm behind the Medium’s Daily Digest over the past year. When we started this work, the Digest was suboptimal —…
Hard vs Soft Filtering and how this applies to Medium’s Recommendation System In this part 3 we’ll see how we modified one of our hard filtering rules and attempted to turn it into a machine learning based “soft filter”. Intro : This is a 4-part series breaking down improvements to the algorithm behind the Medium’s Daily Digest over the past…
25 Jul 2025
By Sofia Guerreiro, Ricardo Ribeiro Pereira, Iker Perez, Jacopo Bono Detecting financial fraud is like finding a moving needle in a shifting haystack . Fraud accounts for a tiny fraction of financial transactions, often less than 0.1%. At the same time, fraudsters are constantly adapting their tactics to evade detection. And this happens within a live and dynamic environment, where…
18 Jun 2025
Unlocking the Power of Customization: How Our Enrichment System Transforms Recommendation Data…
Booking.com EngineeringUnlocking the Power of Customization: How Our Enrichment System Transforms Recommendation Data Enrichments How are accurate property prices on Booking.com connected to machine learning that recommends appealing property photos? What about the number of users who have wishlisted a property? And how can developers assess if their recommendation models effectively boost traveler clicks? None of these pieces of information are…
24 Feb 2025
How we used generative AI to build our year-in-review campaign
28 Jan 2025
Qualitative comparison of image embedding models to power a scalable similar-image replacement system for Canva designs.
16 Dec 2024
Overview The past few months have been exciting times for Slack’s CI infrastructure. After years of developer frustration with Jenkins (everything from security issues to downtime to generally poor UX) internal pressure led us to move a majority of Slack’s CI jobs from Jenkins to GitHub Actions. My intern project at Slack this summer involved…
25 Nov 2024
How we improved Canva’s private design search while respecting the privacy of our community.
8 Nov 2024
Background and motivation In the fast-paced world of software development, having the right tools can make all the difference. At Slack, we’ve been working on a set of AI-powered developer tools that are saving 10,000+ hours of developer time yearly, while meeting our strictest requirements for security, data protection, and compliance. In this post, we’ll…
31 Oct 2024
Here's how machine learning drives business efficiency, from customer insights to fraud detection, powering smarter, faster decisions. The post Machine Learning for business: what are the advantages? appeared first on Erlang Solutions.
12 Aug 2024
By Sérgio Jesus, Inês Silva, Pedro Saleiro, Hugo Ferreira, Pedro Bizarro In this blog post we will visit Aequitas Flow , an Open-Source framework designed to run complete and standardized experiments of Fair ML algorithms. We encourage you to try Aequitas Flow with the Google Colab Notebooks, which are available in the project’s GitHub repository . This blog post is…
21 Jun 2024
In the world of financial services, the bank or financial institution’s relationship with the customer relies on digital trust , which is anchored in two fundamental principles. First, it must ensure the person engaging through digital banking channels is genuinely the individual they claim to be. Second, it must confirm that this person is authorized to complete the intended financial…
18 Apr 2024
At Slack, we’ve long been conservative technologists. In other words, when we invest in leveraging a new category of infrastructure, we do it rigorously. We’ve done this since we debuted machine learning-powered features in 2016, and we’ve developed a robust process and skilled team in the space. Despite that, over the past year we’ve been…
21 Feb 2024
Leveraging Spark 3 and NVIDIA’s GPUs to Reduce Cloud Cost by up to 70% for Big Data Pipelines
PaypalBy Ilay Chen and Tomer Akirav At PayPal, hundreds of thousands of Apache Spark jobs run on an hourly basis, processing petabytes of data and requiring a high volume of resources. To handle the growth of machine learning solutions, PayPal requires scalable environments, cost awareness and constant innovation. This blog explains how Apache Spark 3 and GPUs can help enterprises…
11 Dec 2023
Photo by fabio on Unsplash PayPal supports over 400 million active consumers and merchants worldwide. Every minute there are several thousand payment transactions. To prevent fraud in real-time at such a scale, we need to streamline our ML workflow and feature engineering processes to build strong predictors of behaviors and risk indicators. On top of that, it must be done…
28 Nov 2023
A clustering-based approach to create deep learning datasets in a day Introduction Understanding what’s happening in an image is both an important task, as well as a costly one. In the last few years, the field of computer vision has greatly accelerated due to the advances in neural networks. At Bumble Inc., we see potential value in computer vision for…
31 Aug 2023
The effective use of AI is becoming the next great differentiator for business, but many SMEs are confused about what to adopt and how to adopt it. The post What businesses should consider when adopting AI and machine learning appeared first on Erlang Solutions.
25 Apr 2023
Why Every Developer Should Learn ChatGPT and SudoLang I recently started using an AI Driven Development (AIDD) process that has many benefits: Increased development productivity 10x — 20x , allowing us to take on more projects, and more ambitious challenges that would previously have been too resource-intensive to tackle. Opened up our applications to magical features we could not have…
3 Apr 2023
Running Riteway’s usage example tests in SudoLang running on ChatGPT using GPT-4 I have been a long-time advocate of Test-Driven Development (TDD) because of its many productivity and quality benefits. You can read more about those in “TDD Changed My Life” . When I realized that GPT-4 was capable of following complex instructions, one of the first things I thought…
6 Sept 2022
Slack, as a product, presents many opportunities for recommendation, where we can make suggestions to simplify the user experience and make it more delightful. Each one seems like a terrific use case for machine learning, but it isn’t realistic for us to create a bespoke solution for each. Instead, we developed a unified framework we…
25 Oct 2021
Our friends at Anaconda have posted a joint announcement last week regarding the use of their repository from Microsoft cloud-hosted products. See the full announcement on their website. Today, Anaconda, Inc. announced a collaboration with Microsoft to enable customers to confidently access Anaconda’s curated library of open-source packages within Microsoft Cloud-hosted products and services, including […] The post Anaconda licensing…
9 Jul 2020
A browser is an enormously complex piece of software, and it's always in development. About a year ago, we asked ourselves: how could we do better? Our CI relied heavily on human intervention. What if we could instead correlate patches to tests using historical regression data? Could we use a machine learning algorithm to figure out the optimal set of…
24 Jan 2018
SoundCloud consists of hundreds of millions of tracks, people, albums, and playlists, and navigating this vast collection of music and personalities poses a large challenge, particularly with so many covers, remixes, and original works all in one place.
4 Oct 2017
Here at SoundCloud, we’ve been working on helping our Data Scientists be more effective, happy, and productive. We revamped our organizational structure, clearly defined the role of a Data Scientist and a Data Engineer, introduced working groups to solve common problems (like this), and positioned ourselves to do incredible work! Most recently, we started thinking about the work that a…
5 Jul 2016
Over the last 100 years we have dialed into radio stations at home, on the road, or in the office to access a curated mix of top hits delivered to us by our favorite DJ. With more and more of our daily activities taking place online, we find our source of music now comes from a mix of our mobile…
21 Jun 2016
With more than 125 million tracks from over 12 million creators heard each month on our platform, SoundCloud is uniquely positioned to offer listeners a full spectrum of music discovery. Classic hits, the latest releases, gems from underground talent and the best of what’s up-and-coming – all in one place. How can you make great content discoverable and available at…