by Aryan Mehrawith Farnaz Karimdady Sharifabad, Prasanna Vijayanathan, Chaïna Wade, Vishal Sharma and Mike Schassberger Aim and Purpose — Problem Statement The purpose of this article is to give insights into analyzing and predicting “out of memory” or OOM kills on the Netflix App. Unlike strong compute devices, TVs and set top boxes usually have stronger memory …
By Alex Hutter, Falguni Jhaveri, and Senthil Sayeebaba In a previous post, we described the indexing architecture of Studio Search and how we scaled the architecture by building a config-driven self-service platform that allowed teams in Content Engineering to spin up search indices easily. This post will discuss how Studio Search supports querying the data available …
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By Astha Singhal, Lakshmi Sudheer, Julia Knecht The Application Security teams at Netflix are responsible for securing the software footprint that we create to run the Netflix product, the Netflix studio, and the business. Our customers are product and engineering teams at Netflix that build these software services and platforms. The Netflix cultural values of ‘Context …
At Netflix, we want to entertain the world through creating engaging content and helping members discover the titles they will love. Key to that is understanding causal effects that connect changes we make in the product to indicators of member joy. To measure causal effects we rely heavily on AB testing, but we also leverage quasi-experimentation …
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by Vivek Kaushal At Netflix, we aim to provide recommendations that match our members’ interests. To achieve this, we rely on Machine Learning (ML) algorithms. ML algorithms can be only as good as the data that we provide to it. This post will focus on the large volume of high-quality data stored in Axion — our fact store …
By Alex Hutter, Falguni Jhaveri and Senthil Sayeebaba Over the past few years Content Engineering at Netflix has been transitioning many of its services to use a federated GraphQL platform. GraphQL federation enables domain teams to independently build and operate their own Domain Graph Services (DGS) and, at the same time, connect their domain with …
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By: Ankush Gulati, David GevorkyanAdditional credits: Michael Clark, Gokhan Ozer Intro Netflix has more than 220 million active members who perform a variety of actions throughout each session, ranging from renaming a profile to watching a title. Reacting to these actions in near real-time to keep the experience consistent across devices is critical for ensuring an …
Posted Grigor Aslanyan, Software Engineer, Google Patent documents typically use legal and highly technical language, with context-dependent terms that may have meanings quite different from colloquial usage and even between different documents. The process of using traditional patent search methods (e.g., keyword searching) to search through the corpus of over one hundred million patent documents …
Artificial intelligence (AI) can automatically learn patterns that humans can’t detect, making it a powerful tool for getting more value out of data. A high-performing model starts with high-quality data, but in many cases, datasets have issues such as incorrect labels or unclear examples that contribute to poor model performance. Data quality is a constant …
Read more “Vertex AI Example-based Explanations improve ML via explainability”
The data received at serving time is rarely in the format your model expects. Numerical columns need to be normalized, features created, image bytes decoded, input values validated. Transforming the data can be as important as the prediction itself. That’s why we’re excited to announce custom prediction routines on Vertex AI, which simplify the process …
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