Category Added in a WPeMatico Campaign
Cold start in recommendation systems goes beyond just new user or new item problems—it’s the complete absence of personalized signals at launch. When someone first arrives, or when fresh content appears, there’s no behavioral history to tell the engine what they care about, so everyone ends up in broad generic segments. That not only dampens …
Read more “Boost cold-start recommendations with vLLM on AWS Trainium”
In April, we released Cluster Director, a unified management plane that makes deploying and managing large-scale AI infrastructure simpler and more intuitive than ever before, putting the power of an AI supercomputer at your fingertips. Today, we’re excited to release new features in preview including an intuitive interface, managed Slurm experience, and observability dashboard that …
Read more “New Cluster Director features: Simplified GUI, managed Slurm, advanced observability”
We’re publishing a paper in Nature introducing Aeneas, the first AI model for contextualizing ancient inscriptions.
Knowledge Graphs represent real-world entities and the relationships between them. Multilingual Knowledge Graph Construction (mKGC) refers to the task of automatically constructing or predicting missing entities and links for knowledge graphs in a multilingual setting. In this work, we reformulate the mKGC task as a Question Answering (QA) task and introduce mRAKL: a Retrieval-Augmented Generation …
At the AWS Summit in New York City, we introduced a comprehensive suite of model customization capabilities for Amazon Nova foundation models. Available as ready-to-use recipes on Amazon SageMaker AI, you can use them to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. In this …
Read more “Customize Amazon Nova in Amazon SageMaker AI using Direct Preference Optimization”
Gemini 2.5 Flash-Lite, previously in preview, is now stable and generally available. This cost-efficient model provides high quality in a small size, and includes 2.5 family features like a 1 million-token context window and multimodality.
In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycle (cycle 2). It provided infrastructure …
The best way to learn AI is by building. From finding quick ways to deploy open models to building complex, multi-agentic systems, it’s easy to feel overwhelmed by the sheer volume of resources out there. To that end, we’ve compiled a living, curated collection of our 25+ favorite how-to guides for Google Cloud. This collection …
Our advanced model officially achieved a gold-medal level performance on problems from the International Mathematical Olympiad (IMO), the world’s most prestigious competition for young mathematicians. It earned a total of 35 points by perfectly solving five out of the six problems.
The ever-increasing parameter counts of deep learning models necessitate effective compression techniques for deployment on resource-constrained devices. This paper explores the application of information geometry, the study of density-induced metrics on parameter spaces, to analyze existing methods within the space of model compression, primarily focusing on operator factorization. Adopting this perspective highlights the core challenge: …