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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 […]

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

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

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

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

This post is cowritten with James Luo from BGL. Data analysis is emerging as a high-impact use case for AI agents. According to Anthropic’s 2026 State of AI Agents Report, 60% of organizations rank data analysis and report generation as their most impactful agentic AI applications. 65% of enterprises cite it as a top priority.