Blog

Today, we are excited to introduce a new feature for SageMaker Studio: SOCI (Seekable Open Container Initiative) indexing. SOCI supports lazy loading of container images, where only the necessary parts of an image are downloaded initially rather than the entire container. SageMaker Studio serves as a web Integrated Development Environment (IDE) for end-to-end machine learning (ML) development, …

Introducing SOCI indexing for Amazon SageMaker Studio: Faster container startup times for AI/ML workloads Read More »

Learn more about the AI tips and tools Google shared in 2025. ​ 

Today, Google Cloud dropped its 2026 AI Agent Trends Report. ​ 

This post is co-written with Ranjit Rajan, Abdullahi Olaoye, and Abhishek Sawarkar from NVIDIA. AI’s next frontier isn’t merely smarter chat-based assistants, it’s autonomous agents that reason, plan, and execute across entire systems. But to accomplish this, enterprise developers need to move from prototypes to production-ready AI agents that scale securely. This challenge grows as …

Build and deploy scalable AI agents with NVIDIA NeMo, Amazon Bedrock AgentCore, and Strands Agents Read More »

Building natural voice conversations with AI agents requires complex infrastructure and lots of code from engineering teams. Text-based agent interactions follow a turn-based pattern: a user sends a complete request, waits for the agent to process it, and receives a full response before continuing. Bi-directional streaming removes this constraint by establishing a persistent connection that …

Bi-directional streaming for real-time agent interactions now available in Amazon Bedrock AgentCore Runtime Read More »

We’re expanding our content transparency tools to help you more easily identify AI-generated content. You can now check if a video was edited or created with Google AI d… ​ 

Kaggle’s AI Agents Intensive with Google brought learners together in a no-cost course to build and deploy the next frontier of AI. ​ 

Learn how Gemini 3 powers Google Search with Generative UI, Nano Banana, and interactive graphics. ​ 

Building custom foundation models requires coordinating multiple assets across the development lifecycle such as data assets, compute infrastructure, model architecture and frameworks, lineage, and production deployments. Data scientists create and refine training datasets, develop custom evaluators to assess model quality and safety, and iterate through fine-tuning configurations to optimize performance. As these workflows scale across …

Tracking and managing assets used in AI development with Amazon SageMaker AI  Read More »

A user can conduct machine learning (ML) data experiments in data environments, such as Snowflake, using the Snowpark library. However, tracking these experiments across diverse environments can be challenging due to the difficulty in maintaining a central repository to monitor experiment metadata, parameters, hyperparameters, models, results, and other pertinent information. In this post, we demonstrate …

Track machine learning experiments with MLflow on Amazon SageMaker using Snowflake integration Read More »