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Scaling Appsec at Netflix (Part 2)

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 …

A Survey of Causal Inference Applications at Netflix

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 …

How Netflix Content Engineering makes a federated graph searchable

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 …

Rapid Event Notification System at Netflix

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 …

Announcing the Patent Phrase Similarity Dataset

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 …

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Vertex AI Example-based Explanations improve ML via explainability

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 …

Simplify model serving with custom prediction routines on Vertex AI

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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Google Cloud and Apollo24|7: Building Clinical Decision Support System (CDSS) together

Clinical Decision Support System (CDSS) is an important technology for the healthcare industry that analyzes data to help healthcare professionals make decisions related to patient care. The market size for the global clinical decision support system appears poised for expansion, with one study predicting a compound annual growth rate (CAGR) of 10.4%, from 2022 to …

UN Economic Commission for Africa Engages NVIDIA to Boost Data Science in 10 Nations

NVIDIA is collaborating with the United Nations Economic Commission for Africa (UNECA) to equip governments and developer communities in 10 nations with data science training and technology to support more informed policymaking and accelerate how resources are allocated. The initiative will empower the countries’ national statistical offices — agencies that handle population censuses data, economic …