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Today, we’re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. This represents a paradigm shift in AI application development. Instead of validating JSON responses and writing fallback logic for when they fail, you can move straight to …

Structured outputs on Amazon Bedrock: Schema-compliant AI responses Read More »

Training and deploying large AI models requires advanced distributed computing capabilities, but managing these distributed systems shouldn’t be complex for data scientists and machine learning (ML) practitioners. The command line interface (CLI) and software development kit (SDK) for Amazon SageMaker HyperPod with Amazon Elastic Kubernetes Service (Amazon EKS) orchestration simplify how you manage cluster infrastructure and use …

Manage Amazon SageMaker HyperPod clusters using the HyperPod CLI and SDK Read More »

In the post Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI, we introduced the Amazon Nova LLM-as-a-judge capability, which is a specialized evaluation model available through Amazon SageMaker AI that you can use to systematically measure the relative performance of generative AI systems. SageMaker AI now offers a rubric-based large language …

Evaluate generative AI models with an Amazon Nova rubric-based LLM judge on Amazon SageMaker AI (Part 2) Read More »

This is a guest post co-written with David Meredith and Josh Zacharias from Associa. Associa, North America’s largest community management company, oversees approximately 7.5 million homeowners with 15,000 employees across more than 300 branch offices. The company manages approximately 48 million documents across 26 TB of data, but their existing document management system lacks efficient …

How Associa transforms document classification with the GenAI IDP Accelerator and Amazon Bedrock Read More »

Embedding models power many modern applications—from semantic search and Retrieval-Augmented Generation (RAG) to recommendation systems and content understanding. However, selecting an embedding model requires careful consideration—after you’ve ingested your data, migrating to a different model means re-embedding your entire corpus, rebuilding vector indexes, and validating search quality from scratch. The right embedding model should deliver …

A practical guide to Amazon Nova Multimodal Embeddings Read More »

Learn how Google’s NAI framework uses AI to make technology more adaptive, inclusive and helpful for everyone. ​ 

Google Cloud built an industry-first AI tool to help U.S. Ski and Snowboard athletes. ​ 

Learn more about Google’s new ad that will run during football’s Big Game on February 8. ​ 

Google AI announcements from January ​ 

Marketing teams face major challenges creating campaigns in today’s digital environment. They must navigate through complex data analytics and rapidly changing consumer preferences to produce engaging, personalized content across multiple channels while maintaining brand consistency and working within tight deadlines. Using generative AI can streamline and accelerate the creative process while maintaining alignment with business …

Accelerating your marketing ideation with generative AI – Part 2: Generate custom marketing images from historical references Read More »