Tag: faang

Editor’s Note: This is the fourth post in a series exploring how Palantir customizes infrastructure software for reliable operation at scale. The following is a guest contribution to the Foundations series from the Gotham Core Platform organization, which builds and maintains the bedrock for mission-critical applications within the Gotham ecosystem. This blog post by Kevin Liang, …

Read more “Managing Elasticsearch Reindex at Scale: Performance, Reliability, and Observability”

Authors: Lequn Wang, Jiangwei 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 …

Read more “GenPage: Towards End-to-End Generative Homepage Construction at Netflix”

Social engineering through phishing remains one of the most common tactics for launching cyberattacks. AI-generated phishing email messages now pose a new challenge for security teams managing email systems, significantly raising the risk because of their advanced sophistication. Modern social engineers use generative AI and open source intelligence (OSINT) to craft thousands of unique messages …

Read more “How Amazon Bedrock catches AI-generated phishing”

Government agencies running workloads in AWS GovCloud (US) need AI capabilities that keep pace with the commercial sector. At the same time, they can’t compromise the security and compliance controls their missions require. As open-weight foundation models (FMs) move from experimentation into mission systems, two requirements shape every model decision. First, the model must deliver …

Read more “Run NVIDIA Nemotron and OpenAI GPT OSS models on Amazon Bedrock in AWS GovCloud (US)”

AlloyDB is an AI-native database—it isn’t just a passive data store, it intelligently understands and processes your data. With AlloyDB, you get industry-leading vector and hybrid search, near 100% accurate natural language-to-SQL capabilities to build conversational agents, tools to enable you to build with your agentic IDEs of choice, and the ability to bring the …

Read more “AlloyDB AI Functions – now with revolutionary performance boosts and cost savings”

Today, we’re excited to announce the availability of Anthropic’s most advanced Sonnet model, Claude Sonnet 5, on Amazon Bedrock and Claude Platform on AWS. Claude Sonnet 5 is the first Sonnet model of Anthropic’s latest generation and represents a meaningful step forward. It delivers top-tier intelligence at Sonnet pricing for coding, agents, and everyday professional …

Read more “Introducing Claude Sonnet 5 on AWS: Anthropic’s most capable Sonnet model”

Authors: Lequn Wang, Jiangwei 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 …

Read more “GenPage: Towards End-to-End Generative Homepage Construction at Netflix”

Amazon Quick Sight is a core feature within Amazon Quick — an agentic, AI-powered digital workspace designed to maximize end-user productivity— that provides AI-powered BI capabilities through natural language queries, interactive dashboards, and embedded analytics from trusted enterprise data sources. Amazon Quick Sight assets such as dashboards, analyses, datasets, and data sources can be backed up using the …

Read more “Implement a backup strategy for Amazon Quick Sight BI assets”

We recently announced the preview of the BigQuery AI.AGG() function. With AI.AGG(), you can use natural-language instructions within a single line of SQL to summarize or synthesize information over millions of rows of unstructured or even multimodal data. Summarize millions of rows with one line of SQL: AI.AGG While BigQuery already offers powerful AI functions …

Read more “Synthesize the big picture and analyze trends with BigQuery’s AI.AGG function”