Local Pan-Privacy for Federated Analytics

Pan-privacy was proposed by Dwork et al. (2010) as an approach to designing a private analytics system that retains its privacy properties in the face of intrusions that expose the system’s internal state. Motivated by federated telemetry applications, we study local pan-privacy, where privacy should be retained under repeated unannounced intrusions on the local state. …

ml 18652 image 1

Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon Bedrock

Multimodal fine-tuning represents a powerful approach for customizing foundation models (FMs) to excel at specific tasks that involve both visual and textual information. Although base multimodal models offer impressive general capabilities, they often fall short when faced with specialized visual tasks, domain-specific content, or particular output formatting requirements. Fine-tuning addresses these limitations by adapting models …

1xxVKEve3C0kvgsPmq8tmMg

Data Streaming: Real-time data for real-time decisions

Data Streaming: Real-time data for real-time decisions (Palantir RFx Blog Series, #8) Often the most business critical decisions are also the most time-sensitive. Data streaming technologies let organizations act on information (almost) as quickly as it comes in. Editor’s note: This is the eighth post in the Palantir RFx Blog Series, which explores how organizations can better …

ML 18742 image 1

Amazon Bedrock Model Distillation: Boost function calling accuracy while reducing cost and latency

Amazon Bedrock Model Distillation is generally available, and it addresses the fundamental challenge many organizations face when deploying generative AI: how to maintain high performance while reducing costs and latency. This technique transfers knowledge from larger, more capable foundation models (FMs) that act as teachers to smaller, more efficient models (students), creating specialized models that …