Real Large Action Models, or LAMs, are still in their infancy. Early examples like Google’s RT-2 or consumer devices such as Rabbit R1 exist, but consistent production-grade outcomes remain elusive. LAMs require enormous amounts of diverse training data covering environmental states, actions, and feedback loops. Gathering this data at scale is both expensive and technically […]

Photo by Annie Spratt on Unsplash As we head into the new year, I want to share an article by Andy Warfield called “A little bit uncomfortable”. Andy writes about something we don’t talk about enough–vulnerability and the relationship between fear and growth. His reflection on years of anxiety about public speaking gets at something […]

Can you believe it? We’re nearly at the end of 2025. And what a year it’s been! From re:Invent recap events, to AWS Summits, AWS Innovate, AWS re:Inforce, Community Days, and DevDays and, recently, adding that cherry on the cake, re:Invent 2025, we have lived through a year filled with exciting moments and technology advancements […]

Enterprises across industries are storing more data than ever. More than 5 petabytes (PB) is the new normal and more than 10PB is increasingly common. The theme for unstructured data management in 2026 is more of everything: more data, more investment, more pains and more AI security and risk concerns. Unstructured data growth is being […]

The week after AWS re:Invent builds on the excitement and energy of the event and is a good time to learn more and understand how the recent announcements can help you solve your challenges and unlock new opportunities. As usual, we have you covered with our top announcements of AWS re:Invent 2025 that you can […]

AI spending keeps climbing at an unbelievable pace. Some of the largest U.S. tech companies plan to pour more than $320 billion into AI and supporting infrastructure. With numbers like that, you’d expect breakthrough results everywhere. But when you talk to the teams actually running these systems, you hear a very different story. A lot […]

Organizations face a challenging trade-off when adapting AI models to their specific business needs: settle for generic models that produce average results, or tackle the complexity and expense of advanced model customization. Traditional approaches force a choice between poor performance with smaller models or the high costs of deploying larger model variants and managing complex […]

Today, I’m happy to announce new serverless customization in Amazon SageMaker AI for popular AI models, such as Amazon Nova, DeepSeek, GPT-OSS, Llama, and Qwen. The new customization capability provides an easy-to-use interface for the latest fine-tuning techniques like reinforcement learning, so you can accelerate the AI model customization process from months to days. With […]

Today, we’re announcing two new AI model training features within Amazon SageMaker HyperPod: checkpointless training, an approach that mitigates the need for traditional checkpoint-based recovery by enabling peer-to-peer state recovery, and elastic training, enabling AI workloads to automatically scale based on resource availability. Checkpointless training – Checkpointless training eliminates disruptive checkpoint-restart cycles, maintaining forward training […]

Today, we’re announcing two new capabilities for Amazon S3 Tables: support for the new Intelligent-Tiering storage class that automatically optimizes costs based on access patterns, and replication support to automatically maintain consistent Apache Iceberg table replicas across AWS Regions and accounts without manual sync. Organizations working with tabular data face two common challenges. First, they […]