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Today, we’re sharing how Amazon Bedrock makes it straightforward to customize Amazon Nova models for your specific business needs. As customers scale their AI deployments, they need models that reflect proprietary knowledge and workflows — whether that means maintaining a consistent brand voice in customer communications, handling complex industry-specific workflows or accurately classifying intents in …

Read more “Customize Amazon Nova models with Amazon Bedrock fine-tuning”

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”