Tag: ai/ml

In the world of AI/ML, data is the fuel that drives training and inference workloads. For Google Kubernetes Engine (GKE) users, Cloud Storage FUSE provides high-performance, scalable access to data stored in Google Cloud Storage. However, we learned from customers that getting the maximum performance out of Cloud Storage FUSE can be complex. Today, we …

Read more “New GKE Cloud Storage FUSE Profiles take the guesswork out of configuring AI storage”

About this SeriesFrontend engineering at Palantir goes far beyond building standard web apps. Our engineers design interfaces for mission-critical decision-making, build operational applications that translate insight to action, and create systems that handle massive datasets — thinking not just about what the user needs, but what they need when the network is unreliable, the stakes are high, …

Read more “Frontend Engineering at Palantir: Plotlines in Three.js”

As organizations scale their AI workloads on Amazon Bedrock, understanding what’s driving spending becomes critical. Teams might need to perform chargebacks, investigate cost spikes, and guide optimization decisions, all of which require cost attribution at the workload level. With Amazon Bedrock Projects, you can attribute inference costs to specific workloads and analyze them in AWS …

Read more “Manage AI costs with Amazon Bedrock Projects”

About this SeriesFrontend engineering at Palantir goes far beyond building standard web apps. Our engineers design interfaces for mission-critical decision-making, build operational applications that translate insight to action, and create systems that handle massive datasets — thinking not just about what the user needs, but what they need when the network is unreliable, the stakes are high, …

Read more “Frontend Engineering at Palantir: Building a Backend-less Cross-Application API”

By Ben Sykes In a previous post, we described how Netflix uses Apache Druid to ingest millions of events per second and query trillions of rows, providing the real-time insights needed to ensure a high-quality experience for our members. Since that post, our scale has grown considerably. With our database holding over 10 trillion rows and …

Read more “Stop Answering the Same Question Twice: Interval-Aware Caching for Druid at Netflix Scale”

Enterprises often struggle to onboard new team members at scale. Human resources (HR) teams spend time on manual tasks that delay productivity, such as processing documents to answering repeated questions about benefits and policies. For organizations with many new hires, these steps make it harder to keep onboarding consistent and compliant. Organizations lose substantial amounts …

Read more “Build AI-powered employee onboarding agents with Amazon Quick”

Synchronizing the Senses: Powering Multimodal Intelligence for Video Search By: Meenakshi Jindal and Munya Marazanye Today’s filmmakers capture more footage than ever to maximize their creative options, often generating hundreds, if not thousands, of hours of raw material per season or franchise. Extracting the vital moments needed to craft compelling storylines from this sheer volume of …

Read more “Powering Multimodal Intelligence for Video Search”

In today’s agentic AI environments, the network has a new set of responsibilities. In a traditional application stack, the network mainly moves requests between services. But as discussed in a recent white paper, Cloud Infrastructure in the Agent-Native Era, in an agentic system the network sits in the middle of model calls, tool invocations, agent-to-agent …

Read more “Envoy: A future-ready foundation for agentic AI networking”